,
Yong Wang [ctb],
John Muschelli [ctb]| Title: | MR Spectroscopy Analysis Tools |
|---|---|
| Description: | Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>. |
| Authors: | Martin Wilson [cre, aut] ,
Yong Wang [ctb],
John Muschelli [ctb] |
| Maintainer: | Martin Wilson <[email protected]> |
| License: | GPL-3 |
| Version: | 3.2.0 |
| Built: | 2025-02-14 16:25:25 UTC |
| Source: | https://github.com/martin3141/spant |
spant provides a set of tools for reading, visualising and processing Magnetic Resonance Spectroscopy (MRS) data.
To get started with spant, take a look at the introduction vignette:
vignette("spant-intro", package="spant")
Full list of vignettes:
browseVignettes(package = "spant")
Full list of functions:
help(package = spant, help_type = "html")
An online version of the documentation is available from:
https://martin3141.github.io/spant/
Maintainer: Martin Wilson [email protected] (ORCID)
Other contributors:
Yong Wang [contributor]
John Muschelli [contributor]
Useful links:
Report bugs at https://github.com/martin3141/spant/issues/
Return a list of options for an ABfit analysis.
abfit_opts( init_damping = 5, maxiters = 1024, max_shift_pre = 0.078, max_shift_fine = NULL, max_damping = 15, max_phase = 360, lambda = NULL, ppm_left = 4, ppm_right = 0.2, zp = TRUE, bl_ed_pppm = 2, auto_bl_flex = TRUE, bl_comps_pppm = 15, adaptive_bl_comps_pppm = FALSE, export_sp_fit = FALSE, max_asym = 0.25, max_basis_shift = 0.0078, max_basis_damping = 2, maxiters_pre = 1000, algo_pre = "NLOPT_LN_NELDERMEAD", min_bl_ed_pppm = NULL, max_bl_ed_pppm = 7, auto_bl_flex_n = 20, pre_fit_bl_ed_pppm = 1, remove_lip_mm_prefit = FALSE, pre_align = TRUE, max_pre_align_shift = 0.1, pre_align_ref_freqs = c(2.01, 3.03, 3.22), noise_region = c(-0.5, -2.5), optimal_smooth_criterion = "maic", aic_smoothing_factor = 5, anal_jac = TRUE, pre_fit_ppm_left = 4, pre_fit_ppm_right = 1.8, phi1_optim = FALSE, phi1_init = 0, max_dphi1 = 0.2, max_basis_shift_broad = NULL, max_basis_damping_broad = NULL, ahat_calc_method = "lh_pnnls", prefit_phase_search = TRUE, freq_reg = NULL, freq_reg_naa = NULL, lb_reg = NULL, asym_reg = NULL, output_all_paras = FALSE, output_all_paras_raw = FALSE, input_paras_raw = NULL, optim_lw_only = FALSE, optim_lw_only_limit = 20, lb_init = 0.001, lb_init_approx_fit = FALSE, zf_offset = NULL )abfit_opts( init_damping = 5, maxiters = 1024, max_shift_pre = 0.078, max_shift_fine = NULL, max_damping = 15, max_phase = 360, lambda = NULL, ppm_left = 4, ppm_right = 0.2, zp = TRUE, bl_ed_pppm = 2, auto_bl_flex = TRUE, bl_comps_pppm = 15, adaptive_bl_comps_pppm = FALSE, export_sp_fit = FALSE, max_asym = 0.25, max_basis_shift = 0.0078, max_basis_damping = 2, maxiters_pre = 1000, algo_pre = "NLOPT_LN_NELDERMEAD", min_bl_ed_pppm = NULL, max_bl_ed_pppm = 7, auto_bl_flex_n = 20, pre_fit_bl_ed_pppm = 1, remove_lip_mm_prefit = FALSE, pre_align = TRUE, max_pre_align_shift = 0.1, pre_align_ref_freqs = c(2.01, 3.03, 3.22), noise_region = c(-0.5, -2.5), optimal_smooth_criterion = "maic", aic_smoothing_factor = 5, anal_jac = TRUE, pre_fit_ppm_left = 4, pre_fit_ppm_right = 1.8, phi1_optim = FALSE, phi1_init = 0, max_dphi1 = 0.2, max_basis_shift_broad = NULL, max_basis_damping_broad = NULL, ahat_calc_method = "lh_pnnls", prefit_phase_search = TRUE, freq_reg = NULL, freq_reg_naa = NULL, lb_reg = NULL, asym_reg = NULL, output_all_paras = FALSE, output_all_paras_raw = FALSE, input_paras_raw = NULL, optim_lw_only = FALSE, optim_lw_only_limit = 20, lb_init = 0.001, lb_init_approx_fit = FALSE, zf_offset = NULL )
init_damping |
initial value of the Gaussian global damping parameter (Hz). Very poorly shimmed or high field data may benefit from a larger value. |
maxiters |
The maximum number of iterations to run for the detailed fit. |
max_shift_pre |
The maximum allowable global shift to be applied in the approximate (pre-fit) phases of analysis (ppm). |
max_shift_fine |
The maximum allowable global shift to be applied in the detailed fit phase of analysis (ppm). |
max_damping |
maximum permitted value of the global damping parameter (Hz). |
max_phase |
the maximum absolute permitted value of the global zero-order phase term (degrees). Note, the prefit_phase_search option is not constrained by this term. |
lambda |
manually set the the baseline smoothness parameter. |
ppm_left |
downfield frequency limit for the fitting range (ppm). |
ppm_right |
upfield frequency limit for the fitting range (ppm). |
zp |
zero pad the data to twice the original length before fitting. |
bl_ed_pppm |
manually set the the baseline smoothness parameter (ED per ppm). |
auto_bl_flex |
automatically determine the level of baseline smoothness. |
bl_comps_pppm |
spline basis density (signals per ppm). |
adaptive_bl_comps_pppm |
adjust the spline basis density in the detailed fit phase, based on the required level of smoothness, to reduce computation time. |
export_sp_fit |
add the fitted spline functions to the fit result. |
max_asym |
maximum allowable value of the asymmetry parameter. |
max_basis_shift |
maximum allowable frequency shift for individual basis signals (ppm). |
max_basis_damping |
maximum allowable Lorentzian damping factor for individual basis signals (Hz). |
maxiters_pre |
maximum iterations for the coarse (pre-)fit. |
algo_pre |
optimisation method for the coarse (pre-)fit. |
min_bl_ed_pppm |
minimum value for the candidate baseline flexibility analyses (ED per ppm). |
max_bl_ed_pppm |
minimum value for the candidate baseline flexibility analyses (ED per ppm). |
auto_bl_flex_n |
number of candidate baseline analyses to perform. |
pre_fit_bl_ed_pppm |
level of baseline flexibility to use in the coarse fitting stage of the algorithm (ED per ppm). |
remove_lip_mm_prefit |
remove broad signals in the coarse fitting stage of the algorithm. |
pre_align |
perform a pre-alignment step before coarse fitting. |
max_pre_align_shift |
maximum allowable shift in the pre-alignment step (ppm). |
pre_align_ref_freqs |
a vector of prominent spectral frequencies used in the pre-alignment step (ppm). |
noise_region |
spectral region to estimate the noise level (ppm). |
optimal_smooth_criterion |
method to determine the optimal smoothness. |
aic_smoothing_factor |
modification factor for the AIC calculation. Larger values result in less flexible baselines. |
anal_jac |
use a analytical approximation to the jacobian in the detailed fitting stage. |
pre_fit_ppm_left |
downfield frequency limit for the fitting range in the coarse fitting stage of the algorithm (ppm). |
pre_fit_ppm_right |
upfield frequency limit for the fitting range in the coarse fitting stage of the algorithm (ppm). |
phi1_optim |
apply and optimise a frequency dependant phase term. |
phi1_init |
initial value for the frequency dependant phase term (ms). |
max_dphi1 |
maximum allowable change from the initial frequency dependant phase term (ms). |
max_basis_shift_broad |
maximum allowable shift for broad signals in the basis (ppm). Determined based on their name beginning with Lip or MM. The default value is set to max_basis_shift. |
max_basis_damping_broad |
maximum allowable Lorentzian damping for broad signals in the basis (Hz). Determined based on their name beginning with Lip or MM. The default value is set to max_basis_damping. |
ahat_calc_method |
method to calculate the metabolite amplitudes. May be one of: "lh_pnnls" or "ls". |
prefit_phase_search |
perform a 1D search for the optimal phase in the prefit stage of the algorithm. |
freq_reg |
frequency shift parameter. |
freq_reg_naa |
frequency shift parameter for NAA and NAAG. |
lb_reg |
individual line broadening parameter. |
asym_reg |
lineshape asymmetry parameter. |
output_all_paras |
include more fitting parameters in the fit table, e.g. individual shift and damping factors for each basis set element. |
output_all_paras_raw |
include raw fitting parameters in the fit table. For advanced diagnostic use only. |
input_paras_raw |
input raw fitting parameters. For advanced diagnostic use only. |
optim_lw_only |
optimize the global line-broadening term only. |
optim_lw_only_limit |
limits for the line-breading term as a percentage of the starting value when optim_lw_only is TRUE. |
lb_init |
initial Lorentzian line broadening value (in Hz) for the individual basis signals. Setting to 0 will clash with the minimum allowable value (eg hard constraint) during the detailed fit. |
lb_init_approx_fit |
apply lb_init to the basis during the approximate iterative fit. |
zf_offset |
offset in number of data points from the end of the FID to zero-fill. Default is NULL and will automatically set this to 50 points when the FID distortion flag is set for the mrs_data. |
full list of options.
opts <- abfit_opts(ppm_left = 4.2, noise_region = c(-1, -3))opts <- abfit_opts(ppm_left = 4.2, noise_region = c(-1, -3))
Return a list of options for an ABfit analysis to maintain comparability with analyses performed with version 1.9.0 (and earlier) of spant.
abfit_opts_v1_9_0(...)abfit_opts_v1_9_0(...)
... |
arguments passed to abfit_opts. |
full list of options.
Return a list of options for an ABfit analysis with regularision.
abfit_reg_opts( init_damping = 5, maxiters = 128, max_shift_pre = 0.078, max_shift_fine = 0.05, max_damping = 15, max_phase = 360, lambda = NULL, ppm_left = 4, ppm_right = 0.2, zp = TRUE, bl_ed_pppm = 2, auto_bl_flex = TRUE, bl_comps_pppm = 15, adaptive_bl_comps_pppm = TRUE, export_sp_fit = FALSE, max_asym = Inf, max_basis_shift = Inf, max_basis_damping = Inf, maxiters_pre = 1000, algo_pre = "NLOPT_LN_NELDERMEAD", min_bl_ed_pppm = NULL, max_bl_ed_pppm = 7, auto_bl_flex_n = 20, pre_fit_bl_ed_pppm = 1, remove_lip_mm_prefit = FALSE, pre_align = TRUE, max_pre_align_shift = 0.1, pre_align_ref_freqs = c(2.01, 3.03, 3.22), noise_region = c(-0.5, -2.5), optimal_smooth_criterion = "maic", aic_smoothing_factor = 5, anal_jac = TRUE, pre_fit_ppm_left = 4, pre_fit_ppm_right = 1.8, phi1_optim = FALSE, phi1_init = 0, max_dphi1 = 0.2, max_basis_shift_broad = NULL, max_basis_damping_broad = NULL, ahat_calc_method = "lh_pnnls", prefit_phase_search = TRUE, freq_reg = 0.004, freq_reg_naa = NULL, lb_reg = "lcm_compat", asym_reg = 0.1, output_all_paras = FALSE, output_all_paras_raw = FALSE, input_paras_raw = NULL, optim_lw_only = FALSE, optim_lw_only_limit = 20, lb_init = "lcm_compat", lb_init_approx_fit = FALSE, zf_offset = NULL )abfit_reg_opts( init_damping = 5, maxiters = 128, max_shift_pre = 0.078, max_shift_fine = 0.05, max_damping = 15, max_phase = 360, lambda = NULL, ppm_left = 4, ppm_right = 0.2, zp = TRUE, bl_ed_pppm = 2, auto_bl_flex = TRUE, bl_comps_pppm = 15, adaptive_bl_comps_pppm = TRUE, export_sp_fit = FALSE, max_asym = Inf, max_basis_shift = Inf, max_basis_damping = Inf, maxiters_pre = 1000, algo_pre = "NLOPT_LN_NELDERMEAD", min_bl_ed_pppm = NULL, max_bl_ed_pppm = 7, auto_bl_flex_n = 20, pre_fit_bl_ed_pppm = 1, remove_lip_mm_prefit = FALSE, pre_align = TRUE, max_pre_align_shift = 0.1, pre_align_ref_freqs = c(2.01, 3.03, 3.22), noise_region = c(-0.5, -2.5), optimal_smooth_criterion = "maic", aic_smoothing_factor = 5, anal_jac = TRUE, pre_fit_ppm_left = 4, pre_fit_ppm_right = 1.8, phi1_optim = FALSE, phi1_init = 0, max_dphi1 = 0.2, max_basis_shift_broad = NULL, max_basis_damping_broad = NULL, ahat_calc_method = "lh_pnnls", prefit_phase_search = TRUE, freq_reg = 0.004, freq_reg_naa = NULL, lb_reg = "lcm_compat", asym_reg = 0.1, output_all_paras = FALSE, output_all_paras_raw = FALSE, input_paras_raw = NULL, optim_lw_only = FALSE, optim_lw_only_limit = 20, lb_init = "lcm_compat", lb_init_approx_fit = FALSE, zf_offset = NULL )
init_damping |
initial value of the Gaussian global damping parameter (Hz). Very poorly shimmed or high field data may benefit from a larger value. |
maxiters |
The maximum number of iterations to run for the detailed fit. |
max_shift_pre |
The maximum allowable global shift to be applied in the approximate (pre-fit) phases of analysis (ppm). |
max_shift_fine |
The maximum allowable global shift to be applied in the detailed fit phase of analysis (ppm). |
max_damping |
maximum permitted value of the global damping parameter (Hz). |
max_phase |
the maximum absolute permitted value of the global zero-order phase term (degrees). Note, the prefit_phase_search option is not constrained by this term. |
lambda |
manually set the the baseline smoothness parameter. |
ppm_left |
downfield frequency limit for the fitting range (ppm). |
ppm_right |
upfield frequency limit for the fitting range (ppm). |
zp |
zero pad the data to twice the original length before fitting. |
bl_ed_pppm |
manually set the the baseline smoothness parameter (ED per ppm). |
auto_bl_flex |
automatically determine the level of baseline smoothness. |
bl_comps_pppm |
spline basis density (signals per ppm). |
adaptive_bl_comps_pppm |
adjust the spline basis density in the detailed fit phase, based on the required level of smoothness, to reduce computation time. |
export_sp_fit |
add the fitted spline functions to the fit result. |
max_asym |
maximum allowable value of the asymmetry parameter. |
max_basis_shift |
maximum allowable frequency shift for individual basis signals (ppm). |
max_basis_damping |
maximum allowable Lorentzian damping factor for individual basis signals (Hz). |
maxiters_pre |
maximum iterations for the coarse (pre-)fit. |
algo_pre |
optimisation method for the coarse (pre-)fit. |
min_bl_ed_pppm |
minimum value for the candidate baseline flexibility analyses (ED per ppm). |
max_bl_ed_pppm |
minimum value for the candidate baseline flexibility analyses (ED per ppm). |
auto_bl_flex_n |
number of candidate baseline analyses to perform. |
pre_fit_bl_ed_pppm |
level of baseline flexibility to use in the coarse fitting stage of the algorithm (ED per ppm). |
remove_lip_mm_prefit |
remove broad signals in the coarse fitting stage of the algorithm. |
pre_align |
perform a pre-alignment step before coarse fitting. |
max_pre_align_shift |
maximum allowable shift in the pre-alignment step (ppm). |
pre_align_ref_freqs |
a vector of prominent spectral frequencies used in the pre-alignment step (ppm). |
noise_region |
spectral region to estimate the noise level (ppm). |
optimal_smooth_criterion |
method to determine the optimal smoothness. |
aic_smoothing_factor |
modification factor for the AIC calculation. Larger values result in less flexible baselines. |
anal_jac |
use a analytical approximation to the jacobian in the detailed fitting stage. |
pre_fit_ppm_left |
downfield frequency limit for the fitting range in the coarse fitting stage of the algorithm (ppm). |
pre_fit_ppm_right |
upfield frequency limit for the fitting range in the coarse fitting stage of the algorithm (ppm). |
phi1_optim |
apply and optimise a frequency dependant phase term. |
phi1_init |
initial value for the frequency dependant phase term (ms). |
max_dphi1 |
maximum allowable change from the initial frequency dependant phase term (ms). |
max_basis_shift_broad |
maximum allowable shift for broad signals in the basis (ppm). Determined based on their name beginning with Lip or MM. The default value is set to max_basis_shift. |
max_basis_damping_broad |
maximum allowable Lorentzian damping for broad signals in the basis (Hz). Determined based on their name beginning with Lip or MM. The default value is set to max_basis_damping. |
ahat_calc_method |
method to calculate the metabolite amplitudes. May be one of: "lh_pnnls" or "ls". |
prefit_phase_search |
perform a 1D search for the optimal phase in the prefit stage of the algorithm. |
freq_reg |
frequency shift parameter. |
freq_reg_naa |
frequency shift parameter for NAA and NAAG. |
lb_reg |
individual line broadening parameter. |
asym_reg |
lineshape asymmetry parameter. |
output_all_paras |
include more fitting parameters in the fit table, e.g. individual shift and damping factors for each basis set element. |
output_all_paras_raw |
include raw fitting parameters in the fit table. For advanced diagnostic use only. |
input_paras_raw |
input raw fitting parameters. For advanced diagnostic use only. |
optim_lw_only |
optimize the global line-broadening term only. |
optim_lw_only_limit |
limits for the line-breading term as a percentage of the starting value when optim_lw_only is TRUE. |
lb_init |
initial Lorentzian line broadening value (in Hz) for the individual basis signals. Setting to 0 will clash with the minimum allowable value (eg hard constraint) during the detailed fit. |
lb_init_approx_fit |
apply lb_init to the basis during the approximate iterative fit. |
zf_offset |
offset in number of data points from the end of the FID to zero-fill. Default is NULL and will automatically set this to 50 points when the FID distortion flag is set for the mrs_data. |
full list of options.
opts <- abfit_reg_opts(ppm_left = 4.2, noise_region = c(-1, -3))opts <- abfit_reg_opts(ppm_left = 4.2, noise_region = c(-1, -3))
Simulate pulse sequence acquisition.
acquire(sys, rec_phase = 0, tol = 1e-04, detect = NULL, amp_scale = 1)acquire(sys, rec_phase = 0, tol = 1e-04, detect = NULL, amp_scale = 1)
sys |
spin system object. |
rec_phase |
receiver phase in degrees. |
tol |
ignore resonance amplitudes below this threshold. |
detect |
detection nuclei. |
amp_scale |
scaling factor for the output amplitudes. |
a list of resonance amplitudes and frequencies.
Add noise to an mrs_data object.
add_noise(mrs_data, sd = 0.1, fd = TRUE)add_noise(mrs_data, sd = 0.1, fd = TRUE)
mrs_data |
data to add noise to. |
sd |
standard deviation of the noise. |
fd |
generate the noise samples in the frequency-domain (TRUE) or time-domain (FALSE). This is required since the absolute value of the standard deviation of noise samples changes when data is Fourier transformed. |
mrs_data object with additive normally distributed noise.
Add noise to an mrs_data object to match a given SNR.
add_noise_spec_snr( mrs_data, target_snr, sig_region = c(4, 0.5), ref_data = NULL )add_noise_spec_snr( mrs_data, target_snr, sig_region = c(4, 0.5), ref_data = NULL )
mrs_data |
data to add noise to. |
target_snr |
desired spectral SNR, note this assumes the input data is noise-free, eg simulated data. Note the SNR is estimated from the first scan in the dataset and the same noise level is added to all spectra. |
sig_region |
spectral limits to search for the strongest spectral data point. |
ref_data |
measure the signal from the first scan in this reference data and apply the same target noise level to mrs_data. |
mrs_data object with additive normally distributed noise.
Align spectra to a reference frequency using a convolution based method.
align( mrs_data, ref_freq = 4.65, ref_amp = 1, zf_factor = 2, lb = 2, max_shift = 20, ret_df = FALSE, mean_dyns = FALSE )align( mrs_data, ref_freq = 4.65, ref_amp = 1, zf_factor = 2, lb = 2, max_shift = 20, ret_df = FALSE, mean_dyns = FALSE )
mrs_data |
data to be aligned. |
ref_freq |
reference frequency in ppm units. More than one frequency may be specified. |
ref_amp |
amplitude value for the reference signal. More than one value may be specified to match the number of ref_freq signals. |
zf_factor |
zero filling factor to increase alignment resolution. |
lb |
line broadening to apply to the reference signal. |
max_shift |
maximum allowable shift in Hz. |
ret_df |
return frequency shifts in addition to aligned data (logical). |
mean_dyns |
align the mean spectrum and apply the same shift to each dynamic. |
aligned data object.
Apodise MRSI data in the x-y direction with a k-space filter.
apodise_xy(mrs_data, func = "hamming", w = 2.5)apodise_xy(mrs_data, func = "hamming", w = 2.5)
mrs_data |
MRSI data. |
func |
must be "hamming", "hanning" or "gaussian". |
w |
the reciprocal of the standard deviation for the Gaussian function. |
apodised data.
Combine a pair of basis set objects.
append_basis(basis_a, basis_b)append_basis(basis_a, basis_b)
basis_a |
first basis. |
basis_b |
second basis. |
combined basis set object.
Append MRS data across the coil dimension, assumes they matched across the other dimensions.
append_coils(...)append_coils(...)
... |
MRS data objects as arguments, or a list of MRS data objects. |
a single MRS data object with the input objects concatenated together.
Append MRS data across the dynamic dimension, assumes they matched across the other dimensions.
append_dyns(...)append_dyns(...)
... |
MRS data objects as arguments, or a list of MRS data objects. |
a single MRS data object with the input objects concatenated together.
Append multiple regressor data frames into a single data frame.
append_regs(...)append_regs(...)
... |
input regressor data frames. |
output regressor data frame.
Apply a function over specified array axes.
apply_axes(x, axes, fun, ...)apply_axes(x, axes, fun, ...)
x |
an array. |
axes |
a vector of axes to apply fun over. |
fun |
function to be applied. |
... |
optional arguments to fun. |
array.
z <- array(1:1000, dim = c(10, 10, 10)) a <- apply_axes(z, 3, fft) a[1,1,] == fft(z[1,1,]) a <- apply_axes(z, 3, sum) a[1,1,] == sum(z[1,1,])z <- array(1:1000, dim = c(10, 10, 10)) a <- apply_axes(z, 3, fft) a[1,1,] == fft(z[1,1,]) a <- apply_axes(z, 3, sum) a[1,1,] == sum(z[1,1,])
Apply a function across given dimensions of a MRS data object.
apply_mrs(mrs_data, dims, fun, ..., data_only = FALSE)apply_mrs(mrs_data, dims, fun, ..., data_only = FALSE)
mrs_data |
MRS data. |
dims |
dimensions to apply the function. |
fun |
name of the function. |
... |
arguments to the function. |
data_only |
return an array rather than an MRS data object. |
Simulate an RF pulse on a single spin.
apply_pulse(sys, rho, spin_n, angle, nuc, xy)apply_pulse(sys, rho, spin_n, angle, nuc, xy)
sys |
spin system object. |
rho |
density matrix. |
spin_n |
spin index. |
angle |
RF flip angle in degrees. |
nuc |
nucleus influenced by the pulse. |
xy |
x or y pulse. |
density matrix.
Apply Arg operator to an MRS dataset.
## S3 method for class 'mrs_data' Arg(z)## S3 method for class 'mrs_data' Arg(z)
z |
MRS data. |
MRS data following Arg operator.
Convert a 7 dimensional array in into a mrs_data object. The array dimensions should be ordered as : dummy, X, Y, Z, dynamic, coil, FID.
array2mrs_data( data_array, mrs_data = NULL, fs = NULL, ft = NULL, ref = NULL, nuc = NULL, fd = FALSE )array2mrs_data( data_array, mrs_data = NULL, fs = NULL, ft = NULL, ref = NULL, nuc = NULL, fd = FALSE )
data_array |
7d data array. |
mrs_data |
example data to copy acquisition parameters from. |
fs |
sampling frequency in Hz. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
nuc |
nucleus that is resonant at the transmitter frequency. |
fd |
flag to indicate if the matrix is in the frequency domain (logical). |
mrs_data object.
Perform zeroth-order phase correction based on the minimisation of the squared difference between the real and magnitude components of the spectrum.
auto_phase(mrs_data, xlim = c(4, 1.8), smo_ppm_sd = 0.05, ret_phase = FALSE)auto_phase(mrs_data, xlim = c(4, 1.8), smo_ppm_sd = 0.05, ret_phase = FALSE)
mrs_data |
an object of class |
xlim |
frequency range (default units of PPM) to including in the phase. |
smo_ppm_sd |
Gaussian smoother sd in ppm units. |
ret_phase |
return phase values (logical). |
MRS data object and phase values (optional).
Back extrapolate time-domain data points using an autoregressive model.
back_extrap_ar( mrs_data, extrap_pts, pred_pts = NULL, method = "burg", rem_add = TRUE, ... )back_extrap_ar( mrs_data, extrap_pts, pred_pts = NULL, method = "burg", rem_add = TRUE, ... )
mrs_data |
mrs_data object. |
extrap_pts |
number of points to extrapolate. |
pred_pts |
number of points to base the extrapolation on. |
method |
character string specifying the method to fit the model. Must be one of the strings in the default argument (the first few characters are sufficient). Defaults to "burg". |
rem_add |
remove additional points from the end of the FID to maintain the original length of the dataset. Default to TRUE. |
... |
additional arguments to specific methods, see ?ar. |
back extrapolated data.
Convert a basis object to a dynamic mrs_data object.
basis2dyn_mrs_data(basis, amps, tr)basis2dyn_mrs_data(basis, amps, tr)
basis |
basis set object. |
amps |
a data frame with each column corresponding to a basis element and each row corresponding to each dynamic scan. |
tr |
the dataset repetition time in seconds. |
a dynamic mrs_data object.
Convert a basis object to an mrs_data object - where basis signals are spread across the dynamic dimension.
basis2mrs_data( basis, sum_elements = FALSE, amps = NULL, shifts = NULL, lbs = NULL )basis2mrs_data( basis, sum_elements = FALSE, amps = NULL, shifts = NULL, lbs = NULL )
basis |
basis set object. |
sum_elements |
return the sum of basis elements (logical) |
amps |
a vector of scaling factors to apply to each basis element. |
shifts |
a vector of frequency shifts (in ppm) to apply to each basis element. |
lbs |
a vector of Lorentzian line broadening terms (in Hz) to apply to each basis element. |
an mrs_data object with basis signals spread across the dynamic dimension or summed.
Generate a spline basis, slightly adapted from : "Splines, knots, and penalties", Eilers 2010.
bbase(N, number, deg = 3)bbase(N, number, deg = 3)
N |
number of data points. |
number |
number of spline functions. |
deg |
spline degree : deg = 1 linear, deg = 2 quadratic, deg = 3 cubic. |
spline basis as a matrix.
Eilers P. H. C. and Boelens H. F. M. (2005) Baseline correction with asymmetric least squares smoothing. Leiden Univ. Medical Centre Report.
bc_als(mrs_data, lambda = 10000, p = 0.001, ret_bc_only = TRUE)bc_als(mrs_data, lambda = 10000, p = 0.001, ret_bc_only = TRUE)
mrs_data |
mrs_data object. |
lambda |
controls the baseline flexibility. |
p |
controls the penalty for negative data points. |
ret_bc_only |
return the baseline corrected data only. When FALSE the baseline estimate and input data will be returned. |
baseline corrected data.
Remove a constant baseline offset based on a reference spectral region.
bc_constant(mrs_data, xlim)bc_constant(mrs_data, xlim)
mrs_data |
MRS data. |
xlim |
spectral range containing a flat baseline region to measure the offset. |
baseline corrected data.
Apply and subtract a Gaussian smoother in the spectral domain.
bc_gauss(mrs_data, smo_ppm_sd)bc_gauss(mrs_data, smo_ppm_sd)
mrs_data |
mrs_data object. |
smo_ppm_sd |
Gaussian smoother sd in ppm units. |
smoother subtracted data.
Fit and subtract a polynomial to each spectrum in a dataset.
bc_poly(mrs_data, p_deg = 1)bc_poly(mrs_data, p_deg = 1)
mrs_data |
mrs_data object. |
p_deg |
polynomial degree. |
polynomial subtracted data.
Fit and subtract a smoothing spline to each spectrum in a dataset.
bc_spline(mrs_data, spar = 0.5, nknots = 100)bc_spline(mrs_data, spar = 0.5, nknots = 100)
mrs_data |
mrs_data object. |
spar |
smoothing parameter typically between 0 and 1. |
nknots |
number of spline knots. |
smoothing spline subtracted data.
Covert a beta value in the time-domain to an equivalent linewidth in Hz: x * exp(-i * t * t * beta).
beta2lw(beta)beta2lw(beta)
beta |
beta damping value. |
linewidth value in Hz.
Bin equally spaced spectral regions.
bin_spec(mrs_data, width = 0.05, unit = "ppm")bin_spec(mrs_data, width = 0.05, unit = "ppm")
mrs_data |
data to be "binned". |
width |
bin width. |
unit |
bin width unit, can be "ppm" (default) or "pts". |
binned mrs_data object.
Estimate the correlation matrix for a basis set.
calc_basis_corr_mat(basis, xlim = c(4, 0.2), zf = TRUE)calc_basis_corr_mat(basis, xlim = c(4, 0.2), zf = TRUE)
basis |
basis_set object. |
xlim |
spectral range to use in ppm. |
zf |
zero-fill the basis set. |
correlation matrix.
Estimate the CRLB for each element in a basis set.
calc_basis_crlbs( basis, xlim = c(4, 0.2), zf = TRUE, sd = 1, bl_comp_pppm = NULL )calc_basis_crlbs( basis, xlim = c(4, 0.2), zf = TRUE, sd = 1, bl_comp_pppm = NULL )
basis |
basis_set object. |
xlim |
spectral range to use in ppm. |
zf |
zero-fill the basis set. |
sd |
standard deviation of the noise. |
bl_comp_pppm |
number spline baseline components to append per-ppm. |
a vector of predicted errors.
Calculate the noise correlation between coil elements.
calc_coil_noise_cor(noise_data)calc_coil_noise_cor(noise_data)
noise_data |
|
correlation matrix.
Calculate the noise standard deviation for each coil element.
calc_coil_noise_sd(noise_data)calc_coil_noise_sd(noise_data)
noise_data |
|
array of standard deviations.
Calculate the efficiency of a regressor data frame.
calc_design_efficiency(regressor_df, contrasts)calc_design_efficiency(regressor_df, contrasts)
regressor_df |
input regressor data frame. |
contrasts |
a vector of contrast values. |
Calculate the effective dimensions of a spline smoother from lambda.
calc_ed_from_lambda(spline_basis, deriv_mat, lambda)calc_ed_from_lambda(spline_basis, deriv_mat, lambda)
spline_basis |
spline basis. |
deriv_mat |
derivative matrix. |
lambda |
smoothing parameter. |
the effective dimension value.
Calculate the FWHM of a peak from a vector of intensity values.
calc_peak_info_vec(data_pts, interp_f)calc_peak_info_vec(data_pts, interp_f)
data_pts |
input vector. |
interp_f |
interpolation factor to improve the FWHM estimate. |
a vector of: x position of the highest data point, maximum peak value in the y axis, FWHM in the units of data points.
Perform a polynomial fit, subtract and return the standard deviation of the residuals.
calc_sd_poly(y, degree = 1)calc_sd_poly(y, degree = 1)
y |
array. |
degree |
polynomial degree. |
standard deviation of the fit residuals.
Calculate the sum of squares differences between two mrs_data objects.
calc_spec_diff(mrs_data, ref = NULL, xlim = c(4, 0.5))calc_spec_diff(mrs_data, ref = NULL, xlim = c(4, 0.5))
mrs_data |
mrs_data object. |
ref |
reference mrs_data object to calculate differences. |
xlim |
spectral limits to perform calculation. |
an array of the sum of squared difference values.
SNR is defined as the maximum signal value divided by the standard deviation of the noise.
calc_spec_snr( mrs_data, sig_region = c(4, 0.5), noise_region = c(-0.5, -2.5), p_order = 2, interp_f = 4, full_output = FALSE )calc_spec_snr( mrs_data, sig_region = c(4, 0.5), noise_region = c(-0.5, -2.5), p_order = 2, interp_f = 4, full_output = FALSE )
mrs_data |
an object of class |
sig_region |
a ppm region to define where the maximum signal value should be estimated. |
noise_region |
a ppm region to defined where the noise level should be estimated. |
p_order |
polynomial order to fit to the noise region before estimating the standard deviation. |
interp_f |
interpolation factor to improve detection of the highest signal value. |
full_output |
output signal, noise and SNR values separately. |
The mean noise value is subtracted from the maximum signal value to reduce DC offset bias. A polynomial detrending fit (second order by default) is applied to the noise region before the noise standard deviation is estimated.
an array of SNR values.
Check the TARQUIN binary can be run
check_tqn()check_tqn()
Create a logical circular mask spanning the full extent of an n x n matrix.
circ_mask(d, n, offset = 1)circ_mask(d, n, offset = 1)
d |
diameter of the mask. |
n |
number of matrix rows and columns. |
offset |
offset the mask centre in matrix dimension units. |
logical n x n mask matrix.
Zero all coherence orders other than the one supplied as an argument.
coherence_filter(sys, rho, order = 0)coherence_filter(sys, rho, order = 0)
sys |
spin system object. |
rho |
density matrix. |
order |
coherence order to keep (default is 0). |
density matrix.
Collapse MRS data by concatenating spectra along the dynamic dimension.
collapse_to_dyns(x, rm_masked = FALSE) ## S3 method for class 'mrs_data' collapse_to_dyns(x, rm_masked = FALSE) ## S3 method for class 'fit_result' collapse_to_dyns(x, rm_masked = FALSE)collapse_to_dyns(x, rm_masked = FALSE) ## S3 method for class 'mrs_data' collapse_to_dyns(x, rm_masked = FALSE) ## S3 method for class 'fit_result' collapse_to_dyns(x, rm_masked = FALSE)
x |
data object to be collapsed (mrs_data or fit_result object). |
rm_masked |
remove masked dynamics from the output. |
collapsed data with spectra or fits concatenated along the dynamic dimension.
By default, elements are phased and scaled prior to summation. Where a reference signal is not given, the mean dynamic signal will be used instead.
comb_coils( metab, ref = NULL, noise = NULL, scale = TRUE, scale_method = "sig_noise_sq", sum_coils = TRUE, noise_region = c(-0.5, -2.5), average_ref_dyns = TRUE, ref_pt_index = 1, ret_metab_only = FALSE )comb_coils( metab, ref = NULL, noise = NULL, scale = TRUE, scale_method = "sig_noise_sq", sum_coils = TRUE, noise_region = c(-0.5, -2.5), average_ref_dyns = TRUE, ref_pt_index = 1, ret_metab_only = FALSE )
metab |
MRS data containing metabolite data. |
ref |
MRS data containing reference data (optional). |
noise |
MRS data from a noise scan (optional). |
scale |
option to rescale coil elements based on the first data point (logical). |
scale_method |
one of "sig_noise_sq", "sig_noise" or "sig". |
sum_coils |
sum the coil elements as a final step (logical). |
noise_region |
the spectral region (in ppm) to estimate the noise. |
average_ref_dyns |
take the mean of the reference scans in the dynamic dimension before use. |
ref_pt_index |
time-domain point to use for estimating phase and scaling values. |
ret_metab_only |
return the metabolite data only, even if reference data has been specified. |
MRS data.
Combine MRSI coil data using the GLS method presented by An et al JMRI 37:1445-1450 (2013).
comb_coils_mrsi_gls(metab, noise_pts = 30, noise_mrs = NULL)comb_coils_mrsi_gls(metab, noise_pts = 30, noise_mrs = NULL)
metab |
MRSI data containing metabolite data. |
noise_pts |
number of points from the end of the FIDs to use for noise covariance estimation. |
noise_mrs |
MRS data containing noise information for each coil. |
coil combined MRSI data.
Combine SVS coil data using the GLS method presented by An et al JMRI 37:1445-1450 (2013).
comb_coils_svs_gls( metab, ref = NULL, noise_pts = 256, noise_mrs = NULL, use_mean_sens = TRUE )comb_coils_svs_gls( metab, ref = NULL, noise_pts = 256, noise_mrs = NULL, use_mean_sens = TRUE )
metab |
MRS data containing metabolite data. |
ref |
MRS data containing reference data (optional). |
noise_pts |
number of points from the end of the FIDs to use for noise covariance estimation. |
noise_mrs |
MRS data containing noise information for each coil. |
use_mean_sens |
use the dynamic mean to estimate coil sensitivities. |
coil combined MRS data.
Combine all fitting data points from a list of fits into a single data frame.
comb_fit_list_fit_tables( fit_list, add_extra = TRUE, harmonise_ppm = TRUE, inc_basis_sigs = FALSE, inc_indices = TRUE, add_res_id = TRUE )comb_fit_list_fit_tables( fit_list, add_extra = TRUE, harmonise_ppm = TRUE, inc_basis_sigs = FALSE, inc_indices = TRUE, add_res_id = TRUE )
fit_list |
list of fit_result objects. |
add_extra |
add variables in the extra data frame to the output (TRUE). |
harmonise_ppm |
ensure the ppm scale for each fit is identical to the first. |
inc_basis_sigs |
include the individual fitting basis signals in the output table, defaults to FALSE. |
inc_indices |
include indices such as X, Y and coil in the output, defaults to TRUE. These are generally not useful for SVS analysis. |
add_res_id |
add a res_id column to the output to distinguish between datasets. |
a data frame containing the fit data points.
Combine the fit result tables from a list of fit results.
comb_fit_list_result_tables(fit_list, add_extra = TRUE, add_res_id = TRUE)comb_fit_list_result_tables(fit_list, add_extra = TRUE, add_res_id = TRUE)
fit_list |
a list of fit_result objects. |
add_extra |
add variables in the extra data frame to the output (TRUE). |
add_res_id |
add a res_id column to the output to distinguish between datasets. |
a data frame combine all fit result tables with an additional id column to differentiate between data sets. Any variables in the extra data frame may be optionally added to the result.
Combine all fitting data points into a single data frame.
comb_fit_tables(fit_res, inc_basis_sigs = FALSE, inc_indices = TRUE)comb_fit_tables(fit_res, inc_basis_sigs = FALSE, inc_indices = TRUE)
fit_res |
a single fit_result object. |
inc_basis_sigs |
include the individual fitting basis signals in the output table, defaults to FALSE. |
inc_indices |
include indices such as X, Y and coil in the output, defaults to TRUE. These are generally not useful for SVS analysis. |
a data frame containing the fit data points.
Combine a reference and metabolite mrs_data object.
comb_metab_ref(metab, ref)comb_metab_ref(metab, ref)
metab |
metabolite mrs_data object. |
ref |
reference mrs_data object. |
combined metabolite and reference mrs_data object.
Apply Conj operator to an MRS dataset.
## S3 method for class 'mrs_data' Conj(z)## S3 method for class 'mrs_data' Conj(z)
z |
MRS data. |
MRS data following Conj operator.
Convolve two MRS data objects.
conv_mrs(mrs_data, conv)conv_mrs(mrs_data, conv)
mrs_data |
MRS data to be convolved. |
conv |
convolution data stored as an mrs_data object. |
convolved data.
basis_set object based on a frequency range.Crop basis_set object based on a frequency range.
crop_basis(basis, xlim = c(4, 0.2), scale = "ppm")crop_basis(basis, xlim = c(4, 0.2), scale = "ppm")
basis |
basis_set object to be cropped in the spectral dimension. |
xlim |
range of values to crop in the spectral dimension eg xlim = c(4, 0.2). |
scale |
the units to use for the frequency scale, can be one of: "ppm", "hz" or "points". |
cropped mrs_data object.
mrs_data object based on a frequency range.Crop mrs_data object based on a frequency range.
crop_spec(mrs_data, xlim = c(4, 0.2), scale = "ppm")crop_spec(mrs_data, xlim = c(4, 0.2), scale = "ppm")
mrs_data |
MRS data. |
xlim |
range of values to crop in the spectral dimension eg xlim = c(4, 0.2). |
scale |
the units to use for the frequency scale, can be one of: "ppm", "hz" or "points". |
cropped mrs_data object.
mrs_data object data points in the time-domain.Crop mrs_data object data points in the time-domain.
crop_td_pts(mrs_data, start = NULL, end = NULL)crop_td_pts(mrs_data, start = NULL, end = NULL)
mrs_data |
MRS data. |
start |
starting data point (defaults to 1). |
end |
ending data point (defaults to the last saved point). |
cropped mrs_data object.
mrs_data object data points at the end of the FID.Crop mrs_data object data points at the end of the FID.
crop_td_pts_end(mrs_data, pts)crop_td_pts_end(mrs_data, pts)
mrs_data |
MRS data. |
pts |
number of points to remove from the end of the FID. |
cropped mrs_data object.
mrs_data object data points in the time-domain rounding down to
the next smallest power of two (pot). Data that already has a pot length will
not be changed.Crop mrs_data object data points in the time-domain rounding down to
the next smallest power of two (pot). Data that already has a pot length will
not be changed.
crop_td_pts_pot(mrs_data)crop_td_pts_pot(mrs_data)
mrs_data |
MRS data. |
cropped mrs_data object.
Crop an MRSI dataset in the x-y direction
crop_xy(mrs_data, x_dim, y_dim)crop_xy(mrs_data, x_dim, y_dim)
mrs_data |
MRS data object. |
x_dim |
x dimension output length. |
y_dim |
y dimension output length. |
selected subset of MRS data.
Compute the vector cross product between vectors x and y. Adapted from http://stackoverflow.com/questions/15162741/what-is-rs-crossproduct-function
crossprod_3d(x, y)crossprod_3d(x, y)
x |
vector of length 3. |
y |
vector of length 3. |
vector cross product of x and y.
Decimate an MRS signal to half the original sampling frequency by filtering in the frequency domain before down sampling.
decimate_mrs_fd(mrs_data)decimate_mrs_fd(mrs_data)
mrs_data |
MRS data object. |
decimated data at half the original sampling frequency.
Decimate an MRS signal by filtering in the time domain before downsampling.
decimate_mrs_td(mrs_data, q = 2, n = 4, ftype = "iir")decimate_mrs_td(mrs_data, q = 2, n = 4, ftype = "iir")
mrs_data |
MRS data object. |
q |
integer factor to downsample by (default = 2). |
n |
filter order used in the downsampling. |
ftype |
filter type, "iir" or "fir". |
decimated data.
Deconvolve two MRS data objects.
deconv_mrs(mrs_data_a, mrs_data_b)deconv_mrs(mrs_data_a, mrs_data_b)
mrs_data_a |
MRS data to be deconvolved. |
mrs_data_b |
MRS data to be deconvolved. |
deconvolved data.
Return (and optionally modify using the input arguments) a list of the default acquisition parameters.
def_acq_paras( ft = getOption("spant.def_ft"), fs = getOption("spant.def_fs"), N = getOption("spant.def_N"), ref = getOption("spant.def_ref"), nuc = getOption("spant.def_nuc") )def_acq_paras( ft = getOption("spant.def_ft"), fs = getOption("spant.def_fs"), N = getOption("spant.def_N"), ref = getOption("spant.def_ref"), nuc = getOption("spant.def_nuc") )
ft |
specify the transmitter frequency in Hz. |
fs |
specify the sampling frequency in Hz. |
N |
specify the number of data points in the spectral dimension. |
ref |
specify the reference value for ppm scale. |
nuc |
specify the resonant nucleus. |
A list containing the following elements:
ft transmitter frequency in Hz.
fs sampling frequency in Hz.
N number of data points in the spectral dimension.
ref reference value for ppm scale.
nuc resonant nucleus.
Return the default sampling frequency in Hz.
def_fs()def_fs()
sampling frequency in Hz.
Return the default transmitter frequency in Hz.
def_ft()def_ft()
transmitter frequency in Hz.
Return the default number of data points in the spectral dimension.
def_N()def_N()
number of data points in the spectral dimension.
Return the default nucleus.
def_nuc()def_nuc()
number of data points in the spectral dimension.
Return the default reference value for ppm scale.
def_ref()def_ref()
reference value for ppm scale.
Note this reader is very basic and does not use a DICOM dictionary or try to convert the data to the correct datatype. For a more robust and sophisticated reader use the oro.dicom package.
dicom_reader( input, tags = list(sop_class_uid = "0008,0016"), endian = "little", debug = FALSE )dicom_reader( input, tags = list(sop_class_uid = "0008,0016"), endian = "little", debug = FALSE )
input |
either a file path or raw binary object. |
tags |
a named list of tags to be extracted from the file. eg tags <- list(spec_data = "7FE1,1010", pat_name = "0010,0010") |
endian |
can be "little" or "big". |
debug |
print out some debugging information, can be "little" or "big". |
a list with the same structure as the input, but with tag codes replaced with the corresponding data in a raw format.
Apply the diff operator to an MRS dataset in the FID/spectral dimension.
diff_mrs(mrs_data, ...)diff_mrs(mrs_data, ...)
mrs_data |
MRS data. |
... |
additional arguments to the diff function. |
MRS data following diff operator.
Downsample an MRS signal by a factor of 2 using an FFT "brick-wall" filter.
downsample_mrs_fd(mrs_data)downsample_mrs_fd(mrs_data)
mrs_data |
MRS data object. |
downsampled data.
Downsample an MRS signal by a factor of 2 by removing every other data point in the time-domain. Note, signals outside the new sampling frequency will be aliased.
downsample_mrs_td(mrs_data)downsample_mrs_td(mrs_data)
mrs_data |
MRS data object. |
downsampled data.
Return a time scale vector of acquisition times for a dynamic MRS scan. The first temporal scan is assigned a value of 0.
dyn_acq_times(mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL)dyn_acq_times(mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL)
mrs_data |
MRS data. |
tr |
repetition time. |
Ndyns |
number of dynamic scans stored, potentially less than Ntrans if block averaging has been performed. |
Ntrans |
number of dynamic scans acquired. |
time scale vector in units of seconds.
Apply eddy current correction using the Klose method.
ecc(metab, ref, rev = FALSE)ecc(metab, ref, rev = FALSE)
metab |
MRS data to be corrected. |
ref |
reference dataset. |
rev |
reverse the correction. |
In vivo proton spectroscopy in presence of eddy currents. Klose U. Magn Reson Med. 1990 Apr;14(1):26-30.
corrected data in the time domain.
Create an elliptical mask stored as a matrix of logical values.
elliptical_mask(xN, yN, x0, y0, xr, yr, angle)elliptical_mask(xN, yN, x0, y0, xr, yr, angle)
xN |
number of pixels in the x dimension. |
yN |
number of pixels in the y dimension. |
x0 |
centre of ellipse in the x direction in units of pixels. |
y0 |
centre of ellipse in the y direction in units of pixels. |
xr |
radius in the x direction in units of pixels. |
yr |
radius in the y direction in units of pixels. |
angle |
angle of rotation in degrees. |
logical mask matrix with dimensions fov_yN x fov_xN.
Estimate the standard deviation of the noise from a segment of an mrs_data object.
est_noise_sd(mrs_data, n = 100, offset = 100, p_order = 2)est_noise_sd(mrs_data, n = 100, offset = 100, p_order = 2)
mrs_data |
MRS data object. |
n |
number of data points (taken from the end of array) to use in the estimation. |
offset |
number of final points to exclude from the calculation. |
p_order |
polynomial order to fit to the data before estimating the standard deviation. |
standard deviation array.
Frequency-domain convolution based filter.
fd_conv_filt(mrs_data, K = 25, ext = 1)fd_conv_filt(mrs_data, K = 25, ext = 1)
mrs_data |
MRS data to be filtered. |
K |
window width in data points. |
ext |
point separation for linear extrapolation. |
Apply a Gaussian smoother in the spectral domain.
fd_gauss_smo(mrs_data, smo_ppm_sd)fd_gauss_smo(mrs_data, smo_ppm_sd)
mrs_data |
mrs_data object. |
smo_ppm_sd |
Gaussian smoother sd in ppm units. |
spectrally smoothed data.
Transform frequency-domain data to the time-domain.
fd2td(mrs_data)fd2td(mrs_data)
mrs_data |
MRS data in frequency-domain representation. |
MRS data in time-domain representation.
Search for MRS data files in a BIDS filesystem structure.
find_bids_mrs(path, output_full_path = FALSE)find_bids_mrs(path, output_full_path = FALSE)
path |
path to the directory containing the BIDS structure. |
output_full_path |
output the full normalised data paths. |
data frame containing full paths and information on each MRS file.
Find valid MRS data files recursively from a directory path.
find_mrs_files(dir)find_mrs_files(dir)
dir |
a directory path. |
a vector of valid MRS data files.
fit_result.Extract the fit amplitudes from an object of class fit_result.
fit_amps( x, inc_index = FALSE, sort_names = FALSE, append_common_1h_comb = TRUE )fit_amps( x, inc_index = FALSE, sort_names = FALSE, append_common_1h_comb = TRUE )
x |
|
inc_index |
include columns for the voxel index. |
sort_names |
sort the basis set names alphabetically. |
append_common_1h_comb |
append commonly used 1H metabolite combinations eg tNAA = NAA + NAAG. |
a dataframe of amplitudes.
fit_result.Calculate diagnostic information for object of class fit_result.
fit_diags(x, amps = NULL)fit_diags(x, amps = NULL)
x |
|
amps |
known metabolite amplitudes. |
a dataframe of diagnostic information.
Note that TARQUIN and LCModel require these packages to be installed, and the functions set_tqn_cmd and set_lcm_cmd (respectively) need to be used to specify the location of these software packages.
fit_mrs( metab, basis = NULL, method = "ABFIT", w_ref = NULL, opts = NULL, parallel = FALSE, cl = NULL, time = TRUE, progress = "text", extra = NULL )fit_mrs( metab, basis = NULL, method = "ABFIT", w_ref = NULL, opts = NULL, parallel = FALSE, cl = NULL, time = TRUE, progress = "text", extra = NULL )
metab |
metabolite data. |
basis |
basis class object or character vector to basis file in LCModel .basis format. |
method |
'ABFIT' (default), 'VARPRO', 'VARPRO_3P', 'TARQUIN' or 'LCMODEL'. |
w_ref |
water reference data for concentration scaling (optional). |
opts |
options to pass to the analysis method. |
parallel |
perform analyses in parallel (TRUE or FALSE). |
cl |
a parallel socket cluster required to run analyses in parallel. Eg, cl <- parallel::makeCluster(4). |
time |
measure the time taken for the analysis to complete (TRUE or FALSE). |
progress |
option is passed to plyr::alply function to display a progress bar during fitting. Default value is "text", set to "none" to disable. |
extra |
an optional data frame to provide additional variables for use in subsequent analysis steps, eg id or grouping variables. |
Fitting approaches described in the following references: ABfit Wilson, M. Adaptive baseline fitting for 1H MR spectroscopy analysis. Magn Reson Med 2012;85:13-29.
VARPRO van der Veen JW, de Beer R, Luyten PR, van Ormondt D. Accurate quantification of in vivo 31P NMR signals using the variable projection method and prior knowledge. Magn Reson Med 1988;6:92-98.
TARQUIN Wilson, M., Reynolds, G., Kauppinen, R. A., Arvanitis, T. N. & Peet, A. C. A constrained least-squares approach to the automated quantitation of in vivo 1H magnetic resonance spectroscopy data. Magn Reson Med 2011;65:1-12.
LCModel Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 1993;30:672-679.
MRS analysis object.
fname <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") svs <- read_mrs(fname) ## Not run: basis <- sim_basis_1h_brain_press(svs) fit_result <- fit_mrs(svs, basis) ## End(Not run)fname <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") svs <- read_mrs(fname) ## Not run: basis <- sim_basis_1h_brain_press(svs) fit_result <- fit_mrs(svs, basis) ## End(Not run)
Write fit results table to a csv file.
fit_res2csv(fit_res, fname, unscaled = FALSE)fit_res2csv(fit_res, fname, unscaled = FALSE)
fit_res |
fit result object. |
fname |
filename of csv file. |
unscaled |
output the unscaled result table (default = FALSE). |
Note this function is still under development and liable to changes.
fit_svs( input, w_ref = NULL, output_dir = NULL, external_basis = NULL, p_vols = NULL, format = NULL, pul_seq = NULL, TE = NULL, TR = NULL, TE1 = NULL, TE2 = NULL, TE3 = NULL, TM = NULL, append_basis = NULL, remove_basis = NULL, pre_align = TRUE, dfp_corr = TRUE, output_ratio = NULL, ecc = FALSE, hsvd_width = NULL, fit_method = NULL, fit_opts = NULL, fit_subset = NULL, legacy_ws = FALSE, w_att = 0.7, w_conc = 35880, use_basis_cache = "auto", summary_measures = NULL, dyn_av_block_size = NULL, dyn_av_scheme = NULL, dyn_av_scheme_file = NULL, lcm_bin_path = NULL, plot_ppm_xlim = NULL, verbose = FALSE )fit_svs( input, w_ref = NULL, output_dir = NULL, external_basis = NULL, p_vols = NULL, format = NULL, pul_seq = NULL, TE = NULL, TR = NULL, TE1 = NULL, TE2 = NULL, TE3 = NULL, TM = NULL, append_basis = NULL, remove_basis = NULL, pre_align = TRUE, dfp_corr = TRUE, output_ratio = NULL, ecc = FALSE, hsvd_width = NULL, fit_method = NULL, fit_opts = NULL, fit_subset = NULL, legacy_ws = FALSE, w_att = 0.7, w_conc = 35880, use_basis_cache = "auto", summary_measures = NULL, dyn_av_block_size = NULL, dyn_av_scheme = NULL, dyn_av_scheme_file = NULL, lcm_bin_path = NULL, plot_ppm_xlim = NULL, verbose = FALSE )
input |
path or mrs_data object containing MRS data. |
w_ref |
path or mrs_data object containing MRS water reference data. |
output_dir |
directory path to output fitting results. |
external_basis |
precompiled basis set object to use for analysis. |
p_vols |
a numeric vector of partial volumes expressed as percentages. Defaults to 100% white matter. A voxel containing 100% gray matter tissue would use : p_vols = c(WM = 0, GM = 100, CSF = 0). |
format |
Override automatic data format detection. See format argument
in |
pul_seq |
Pulse sequence to use for basis simulation. Can be one of the following values : "press", "press_ideal", "press_shaped", "steam" or "slaser". If "press" then "press_ideal" will be assumed unless the magnetic field is stronger that 2.8 Tesla, "press_shaped" will be assumed for 2.9 Tesla and above. |
TE |
metabolite mrs data echo time in seconds. If not supplied this will be guessed from the metab data file. |
TR |
metabolite mrs data repetition time in seconds. If not supplied this will be guessed from the metab data file. |
TE1 |
PRESS or sLASER sequence timing parameter in seconds. |
TE2 |
PRESS or sLASER sequence timing parameter in seconds. |
TE3 |
sLASER sequence timing parameter in seconds. |
TM |
STEAM mixing time parameter in seconds. |
append_basis |
names of extra signals to add to the default basis. Eg append_basis = c("peth", "cit"). Cannot be used with precompiled basis sets. |
remove_basis |
grep expression to match names of signals to remove from the basis. For example: use "*" to remove all signals, "^mm|^lip" to remove all macromolecular and lipid signals, "^lac" to remove lactate. This operation is performed before signals are added with append_basis. Cannot be used with precompiled basis sets. |
pre_align |
perform simple frequency alignment to known reference peaks. |
dfp_corr |
perform dynamic frequency and phase correction using the RATS method. |
output_ratio |
optional string to specify a metabolite ratio to output. Defaults to "tCr". Multiple metabolites may be specified for multiple outputs. Set to NA to omit. |
ecc |
option to perform water reference based eddy current correction, defaults to FALSE. |
hsvd_width |
set the width of the HSVD filter in Hz. Note the applied width is between -width and +width Hz, with 0 Hz being defined at the centre of the spectral width. Default is disabled (set to NULL), 30 Hz is a reasonable value. |
fit_method |
can be "ABFIT-REG" or "LCMODEL. Defaults to "ABFIT-REG". |
fit_opts |
options to pass to the fitting method. |
fit_subset |
specify a subset of dynamics to analyse, for example 1:16 would only fit the first 16 dynamic scans. |
legacy_ws |
perform and output legacy water scaling compatible with default LCModel and TARQUIN behaviour. See w_att and w_conc arguments to change the default assumptions. Default value is FALSE. |
w_att |
water attenuation factor (default = 0.7) for legacy water scaling. Assumes water T2 of 80ms and a TE = 30 ms. exp(-30ms / 80ms) ~ 0.7. |
w_conc |
assumed water concentration (default = 35880) for legacy water scaling. Default value corresponds to typical white matter. Set to 43300 for gray matter, and 55556 for phantom measurements. |
use_basis_cache |
Pre-cache basis sets to reduce analysis speed. Can be one of the following : "auto", "all" or "none". The default value of "auto" will only use the cache for 3T PRESS - which generally requires more detailed simulation due to high CSD. |
summary_measures |
output an additional table with a subset of metabolite levels, eg c("tNAA", "tNAA/tCr", "tNAA/tCho", "Lac/tNAA"). |
dyn_av_block_size |
perform temporal averaging with the specified block size. Defaults to NULL, eg average across all dynamic scans. |
dyn_av_scheme |
a numeric vector of sequential integers (starting at 1), with the same length as the number of dynamic scans in the metabolite data. For example: c(1, 1, 2, 1, 1, 3, 1, 1). |
dyn_av_scheme_file |
a file path containing a single column of sequential integers (starting at 1) with the same length as the number of dynamic scans in the metabolite data. File may be formatted as .xlsx, .xls, text or csv format. |
lcm_bin_path |
set the path to LCModel binary. |
plot_ppm_xlim |
plotting ppm axis limits in the html results. results. |
verbose |
output potentially useful information. |
metab <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") w_ref <- system.file("extdata", "philips_spar_sdat_W.SDAT", package = "spant") out_dir <- file.path("~", "fit_svs_result") ## Not run: fit_result <- fit_svs(metab, w_ref, out_dir) ## End(Not run)metab <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") w_ref <- system.file("extdata", "philips_spar_sdat_W.SDAT", package = "spant") out_dir <- file.path("~", "fit_svs_result") ## Not run: fit_result <- fit_svs(metab, w_ref, out_dir) ## End(Not run)
GUI interface for the standard SVS 1H brain analysis pipeline, this is a work in progress, and not ready for serious use.
fit_svs_gui()fit_svs_gui()
Fit a T1 recovery curve, from multiple TIs, to a set of amplitudes.
fit_t1_ti_array( ti_vec, amp_vec, lower = 0, upper = 10, output_fit_res = 0.01, ret_full = TRUE )fit_t1_ti_array( ti_vec, amp_vec, lower = 0, upper = 10, output_fit_res = 0.01, ret_full = TRUE )
ti_vec |
vector of TI values in seconds. |
amp_vec |
vector of amplitudes. |
lower |
minimum allowable T1 value. |
upper |
maximum allowable T1 value. |
output_fit_res |
temporal resolution (seconds) of the ideal output relaxation curve. |
ret_full |
return full fitting information including ideal relaxation curve. |
a list containing relaxation parameters and an ideal curve for fit evaluation.
Fit a T1 recovery curve, from multiple TRs, to a set of amplitudes.
fit_t1_tr_array( tr_vec, amp_vec, lower = 0, upper = 10, output_fit_res = 0.01, ret_full = TRUE )fit_t1_tr_array( tr_vec, amp_vec, lower = 0, upper = 10, output_fit_res = 0.01, ret_full = TRUE )
tr_vec |
vector of TR values in seconds. |
amp_vec |
vector of amplitudes. |
lower |
minimum allowable T1 value. |
upper |
maximum allowable T1 value. |
output_fit_res |
temporal resolution (seconds) of the ideal output relaxation curve. |
ret_full |
return full fitting information including ideal relaxation curve. |
a list containing relaxation parameters and an ideal curve for fit evaluation.
Fit a T2 relaxation curve, from multiple TEs, to a set of amplitudes.
fit_t2_te_array( te_vec, amp_vec, lower = 0, upper = 10, output_fit_res = 0.01, ret_full = TRUE )fit_t2_te_array( te_vec, amp_vec, lower = 0, upper = 10, output_fit_res = 0.01, ret_full = TRUE )
te_vec |
vector of TE values in seconds. |
amp_vec |
vector of amplitudes. |
lower |
minimum allowable T2 value. |
upper |
maximum allowable T2 value. |
output_fit_res |
temporal resolution (seconds) of the ideal output relaxation curve. |
ret_full |
return full fitting information including ideal relaxation curve. |
a list containing relaxation parameters and an ideal curve for fit evaluation.
Return the phase of the first data point in the time-domain.
fp_phase(mrs_data)fp_phase(mrs_data)
mrs_data |
MRS data. |
phase values in degrees.
Perform a zeroth order phase correction based on the phase of the first data point in the time-domain.
fp_phase_correct(mrs_data, ret_phase = FALSE)fp_phase_correct(mrs_data, ret_phase = FALSE)
mrs_data |
MRS data to be corrected. |
ret_phase |
return phase values (logical). |
corrected data or a list with corrected data and optional phase values.
Scale the first time-domain data point in an mrs_data object.
fp_scale(mrs_data, scale = 0.5)fp_scale(mrs_data, scale = 0.5)
mrs_data |
MRS data. |
scale |
scaling value, defaults to 0.5. |
scaled mrs_data object.
Return the sampling frequency in Hz of an MRS dataset.
fs(mrs_data)fs(mrs_data)
mrs_data |
MRS data. |
sampling frequency in Hz.
Apply the Fourier transform over the dynamic dimension.
ft_dyns(mrs_data, ft_shift = FALSE, ret_mod = FALSE, fd = TRUE)ft_dyns(mrs_data, ft_shift = FALSE, ret_mod = FALSE, fd = TRUE)
mrs_data |
MRS data where the dynamic dimension is in the time-domain. |
ft_shift |
apply FT shift to the output, default is FALSE. |
ret_mod |
return the modulus out the transform, default is FALSE. |
fd |
transform the chemical shift axis to the frequency domain first, default is TRUE. |
transformed MRS data.
Perform a fft and ffshift on a vector.
ft_shift(vec_in)ft_shift(vec_in)
vec_in |
vector input. |
output vector.
Perform a fft and fftshift on a matrix with each column replaced by its shifted fft.
ft_shift_mat(mat_in)ft_shift_mat(mat_in)
mat_in |
matrix input. |
output matrix.
Create a two dimensional Gaussian window function stored as a matrix.
gausswin_2d(xN, yN, x0, y0, xw, yw)gausswin_2d(xN, yN, x0, y0, xw, yw)
xN |
number of pixels in the x dimension. |
yN |
number of pixels in the y dimension. |
x0 |
centre of window function in the x direction in units of pixels. Note, only integer values are applied. |
y0 |
centre of window function in the y direction in units of pixels. Note, only integer values are applied. |
xw |
the reciprocal of the standard deviation of the Gaussian window in x direction. |
yw |
the reciprocal of the standard deviation of the Gaussian window in y direction. |
matrix with dimensions fov_yN x fov_xN.
Generate baseline regressor.
gen_baseline_reg(mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL)gen_baseline_reg(mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL)
mrs_data |
mrs_data object for timing information. |
tr |
repetition time. |
Ndyns |
number of dynamic scans stored, potentially less than Ntrans if block averaging has been performed. |
Ntrans |
number of dynamic scans acquired. |
a single baseline regressor with value of 1.
Generate BOLD regressors.
gen_bold_reg( onset, duration = NULL, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL, match_tr = TRUE, dt = 0.1, normalise = FALSE )gen_bold_reg( onset, duration = NULL, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL, match_tr = TRUE, dt = 0.1, normalise = FALSE )
onset |
stimulus onset in seconds. |
duration |
stimulus duration in seconds. |
trial_type |
string label for the stimulus. |
mrs_data |
mrs_data object for timing information. |
tr |
repetition time. |
Ndyns |
number of dynamic scans stored, potentially less than Ntrans if block averaging has been performed. |
Ntrans |
number of dynamic scans acquired. |
match_tr |
match the output to the input mrs_data. |
dt |
timing resolution for internal calculations. |
normalise |
normalise the response function to have a maximum value of one. |
BOLD regressor data frame.
Generate regressors by convolving a specified response function with a stimulus.
gen_conv_reg( onset, duration = NULL, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL, resp_fn, match_tr = TRUE, normalise = FALSE )gen_conv_reg( onset, duration = NULL, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL, resp_fn, match_tr = TRUE, normalise = FALSE )
onset |
stimulus onset in seconds. |
duration |
stimulus duration in seconds. |
trial_type |
string label for the stimulus. |
mrs_data |
mrs_data object for timing information. |
tr |
repetition time. |
Ndyns |
number of dynamic scans stored, potentially less than Ntrans if block averaging has been performed. |
Ntrans |
number of dynamic scans acquired. |
resp_fn |
a data frame specifying the response function to be convolved. |
match_tr |
match the output to the input mrs_data. |
normalise |
normalise the response function to have a maximum value of one. |
BOLD regressor data frame.
Generate the F product operator.
gen_F(sys, op, detect = NULL)gen_F(sys, op, detect = NULL)
sys |
spin system object. |
op |
operator, one of "x", "y", "z", "p", "m". |
detect |
detection nuclei. |
F product operator matrix.
Generate the Fxy product operator with a specified phase.
gen_F_xy(sys, phase, detect = NULL)gen_F_xy(sys, phase, detect = NULL)
sys |
spin system object. |
phase |
phase angle in degrees. |
detect |
detection nuclei. |
product operator matrix.
Expand a regressor matrix for a group analysis.
gen_group_reg(regressor_df, n)gen_group_reg(regressor_df, n)
regressor_df |
input regressor data frame. |
n |
number of datasets n the group. |
Generate the I product operator for a single spin.
gen_I(n, spin_num, op)gen_I(n, spin_num, op)
n |
spin index number for the required operator. |
spin_num |
vector of spin numbers in the system. |
op |
operator, one of "x", "y", "z", "p", "m". |
I product operator matrix.
Generate impulse regressors.
gen_impulse_reg( onset, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL )gen_impulse_reg( onset, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL )
onset |
stimulus onset in seconds. |
trial_type |
string label for the stimulus. |
mrs_data |
mrs_data object for timing information. |
tr |
repetition time. |
Ndyns |
number of dynamic scans stored, potentially less than Ntrans if block averaging has been performed. |
Ntrans |
number of dynamic scans acquired. |
impulse regressors data frame.
Generate polynomial regressors.
gen_poly_reg(degree, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL)gen_poly_reg(degree, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL)
degree |
the degree of the polynomial. |
mrs_data |
mrs_data object for timing information. |
tr |
repetition time. |
Ndyns |
number of dynamic scans stored, potentially less than Ntrans if block averaging has been performed. |
Ntrans |
number of dynamic scans acquired. |
polynomial regressors.
Generate trapezoidal regressors.
gen_trap_reg( onset, duration, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL, rise_t = 0, fall_t = 0, exp_fall = FALSE, exp_fall_power = 1, smo_sigma = NULL, match_tr = TRUE, dt = 0.01, normalise = FALSE )gen_trap_reg( onset, duration, trial_type = NULL, mrs_data = NULL, tr = NULL, Ndyns = NULL, Ntrans = NULL, rise_t = 0, fall_t = 0, exp_fall = FALSE, exp_fall_power = 1, smo_sigma = NULL, match_tr = TRUE, dt = 0.01, normalise = FALSE )
onset |
stimulus onset in seconds. |
duration |
stimulus duration in seconds. |
trial_type |
string label for the stimulus. |
mrs_data |
mrs_data object for timing information. |
tr |
repetition time. |
Ndyns |
number of dynamic scans stored, potentially less than Ntrans if block averaging has been performed. |
Ntrans |
number of dynamic scans acquired. |
rise_t |
time to reach a plateau from baseline in seconds. |
fall_t |
time to fall from plateau level back to baseline in seconds. |
exp_fall |
model an exponential fall instead of linear. |
exp_fall_power |
exponential fall power. |
smo_sigma |
standard deviation of Gaussian smoothing kernel in seconds. Set to NULL to disable (default behavior). |
match_tr |
match the output to the input mrs_data. |
dt |
timing resolution for internal calculations. |
normalise |
normalise the response function to have a maximum value of one. |
trapezoidal regressor data frame.
Note, this is a basic set and it may be appropriate to also include Asc, Gly and PEth for high quality MRS data.
get_1h_brain_basis_names(add = NULL, remove = NULL, inc_lip_mm = TRUE)get_1h_brain_basis_names(add = NULL, remove = NULL, inc_lip_mm = TRUE)
add |
optional character vector of additional molecular names. Eg c("asc", "gly", "peth"). |
remove |
optional character vector of molecular names to remove from the set. Eg c("m_cr_ch2"). |
inc_lip_mm |
include Lipid and MM basis signals. |
a character vector of molecule names.
mol_parameter objects suitable for 1H brain MRS
analyses.Return a list of mol_parameter objects suitable for 1H brain MRS
analyses.
get_1h_brain_basis_paras(ft, metab_lw = NULL, lcm_compat = FALSE)get_1h_brain_basis_paras(ft, metab_lw = NULL, lcm_compat = FALSE)
ft |
transmitter frequency in Hz. |
metab_lw |
linewidth of metabolite signals (Hz). |
lcm_compat |
when TRUE, lipid, MM and -CrCH molecules will be excluded from the output. |
list of mol_parameter objects.
mol_parameter objects suitable for 1H brain MRS
analyses.Return a list of mol_parameter objects suitable for 1H brain MRS
analyses.
get_1h_brain_basis_paras_v1(ft, metab_lw = NULL, lcm_compat = FALSE)get_1h_brain_basis_paras_v1(ft, metab_lw = NULL, lcm_compat = FALSE)
ft |
transmitter frequency in Hz. |
metab_lw |
linewidth of metabolite signals (Hz). |
lcm_compat |
when TRUE, lipid, MM and -CrCH molecules will be excluded from the output. |
list of mol_parameter objects.
mol_parameter objects suitable for 1H brain MRS
analyses.Return a list of mol_parameter objects suitable for 1H brain MRS
analyses.
get_1h_brain_basis_paras_v2(ft, metab_lw = NULL, lcm_compat = FALSE)get_1h_brain_basis_paras_v2(ft, metab_lw = NULL, lcm_compat = FALSE)
ft |
transmitter frequency in Hz. |
metab_lw |
linewidth of metabolite signals (Hz). |
lcm_compat |
when TRUE, lipid, MM and -CrCH molecules will be excluded from the output. |
list of mol_parameter objects.
mol_parameter objects suitable for 1H brain MRS
analyses.Return a list of mol_parameter objects suitable for 1H brain MRS
analyses.
get_1h_brain_basis_paras_v3(ft, metab_lw = NULL, lcm_compat = FALSE)get_1h_brain_basis_paras_v3(ft, metab_lw = NULL, lcm_compat = FALSE)
ft |
transmitter frequency in Hz. |
metab_lw |
linewidth of metabolite signals (Hz). |
lcm_compat |
when TRUE, lipid, MM and -CrCH molecules will be excluded from the output. |
list of mol_parameter objects.
Return a character vector of molecules included in the GE BRAINO phantom.
get_1h_braino_basis_names()get_1h_braino_basis_names()
a character vector of molecule names.
Return a character vector of molecules included in the Gold Star Phantoms SPECTRE phantom.
get_1h_spectre_basis_names()get_1h_spectre_basis_names()
a character vector of molecule names.
Get the point spread function (PSF) for a 2D phase encoded MRSI scan.
get_2d_psf( FOV = 160, mat_size = 16, sampling = "circ", hamming = FALSE, ensure_odd = TRUE )get_2d_psf( FOV = 160, mat_size = 16, sampling = "circ", hamming = FALSE, ensure_odd = TRUE )
FOV |
field of view in mm. |
mat_size |
acquisition matrix size (not interpolated). |
sampling |
can be either "circ" for circular or "rect" for rectangular. |
hamming |
should Hamming k-space weighting be applied (default FALSE). |
ensure_odd |
add 1mm to the FOV when required to ensure the output pdf has odd dimensions. Required when using get_mrsi2d_seg. |
A matrix of the PSF with 1mm resolution.
Return acquisition parameters from a MRS data object.
get_acq_paras(mrs_data)get_acq_paras(mrs_data)
mrs_data |
MRS data. |
list of acquisition parameters.
Return a subset of the input basis.
get_basis_subset(basis, names, invert = FALSE)get_basis_subset(basis, names, invert = FALSE)
basis |
input basis. |
names |
basis set elements to keep in the returned object. |
invert |
set to true to return all basis elements except those given in the names argument. |
a subset of the input basis.
Extract a subset of dynamic scans.
get_dyns(mrs_data, subset)get_dyns(mrs_data, subset)
mrs_data |
dynamic MRS data. |
subset |
vector containing indices to the dynamic scans to be returned. |
MRS data containing the subset of requested dynamics.
Return even numbered dynamic scans starting from 1 (2,4,6...).
get_even_dyns(mrs_data)get_even_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
dynamic MRS data containing even numbered scans.
Return the first half of a dynamic series.
get_fh_dyns(mrs_data)get_fh_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
first half of the dynamic series.
Get a data array from a fit result.
get_fit_map(fit_res, name)get_fit_map(fit_res, name)
fit_res |
|
name |
name of the quantity to plot, eg "tNAA". |
Return the first time-domain data point.
get_fp(mrs_data)get_fp(mrs_data)
mrs_data |
MRS data. |
first time-domain data point.
Generate a gaussian pulse shape.
get_guassian_pulse(angle, n, trunc = 1)get_guassian_pulse(angle, n, trunc = 1)
angle |
pulse angle in degrees. |
n |
number of points to generate. |
trunc |
percentage truncation factor. |
Return the first scans of a dynamic series.
get_head_dyns(mrs_data, n = 1)get_head_dyns(mrs_data, n = 1)
mrs_data |
dynamic MRS data. |
n |
the number of dynamic scans to return. |
first scans of a dynamic series.
Generate a double gamma model of the HRF as used in SPM.
get_hrf(end_t = 30, res_t = 0.01)get_hrf(end_t = 30, res_t = 0.01)
end_t |
last time point to generate in seconds. |
res_t |
temporal resolution in seconds, defaults to 10ms. |
a data.frame of time and HRF vectors.
Print the command to run the LCModel command-line program.
get_lcm_cmd()get_lcm_cmd()
Extract the metabolite component from an mrs_data object.
get_metab(mrs_data)get_metab(mrs_data)
mrs_data |
MRS data. |
metabolite component.
get_mol_paras function.Return a character array of names that may be used with the
get_mol_paras function.
get_mol_names()get_mol_names()
a character array of names.
mol_parameters object for a named molecule.Get a mol_parameters object for a named molecule.
get_mol_paras(name, ...)get_mol_paras(name, ...)
name |
the name of the molecule. |
... |
arguments to pass to molecule definition function. |
Generate an affine for nifti generation.
get_mrs_affine(mrs_data, x_pos = 1, y_pos = 1, z_pos = 1)get_mrs_affine(mrs_data, x_pos = 1, y_pos = 1, z_pos = 1)
mrs_data |
input data. |
x_pos |
x_position coordinate. |
y_pos |
y_position coordinate. |
z_pos |
z_position coordinate. |
affine matrix.
mrs_data object.Generate a MRSI VOI from an mrs_data object.
get_mrsi_voi(mrs_data, target_mri = NULL, map = NULL, ker = mmand::boxKernel())get_mrsi_voi(mrs_data, target_mri = NULL, map = NULL, ker = mmand::boxKernel())
mrs_data |
MRS data. |
target_mri |
optional image data to match the intended volume space. |
map |
optional voi intensity map. |
ker |
kernel to rescale the map data to the target_mri. Default value is mmand::boxKernel(), use mmand::mnKernel() for a smoothed map. |
volume data as a nifti object.
mrs_data object.Generate a MRSI voxel from an mrs_data object.
get_mrsi_voxel(mrs_data, target_mri, x_pos, y_pos, z_pos)get_mrsi_voxel(mrs_data, target_mri, x_pos, y_pos, z_pos)
mrs_data |
MRS data. |
target_mri |
optional image data to match the intended volume space. |
x_pos |
x voxel coordinate. |
y_pos |
y voxel coordinate. |
z_pos |
z voxel coordinate. |
volume data as a nifti object.
mrs_data object.Generate a MRSI voxel PSF from an mrs_data object.
get_mrsi_voxel_xy_psf(mrs_data, target_mri, x_pos, y_pos, z_pos)get_mrsi_voxel_xy_psf(mrs_data, target_mri, x_pos, y_pos, z_pos)
mrs_data |
MRS data. |
target_mri |
optional image data to match the intended volume space. |
x_pos |
x voxel coordinate. |
y_pos |
y voxel coordinate. |
z_pos |
z voxel coordinate. |
volume data as a nifti object.
Localisation is assumed to be perfect in the z direction and determined by the ker input in the x-y direction.
get_mrsi2d_seg(mrs_data, mri_seg, ker)get_mrsi2d_seg(mrs_data, mri_seg, ker)
mrs_data |
2D MRSI data with multiple voxels in the x-y dimension. |
mri_seg |
MRI data with values corresponding to the segmentation class. Must be 1mm isotropic resolution. |
ker |
MRSI PSF kernel in the x-y direction compatible with the mmand package, eg: mmand::shapeKernel(c(10, 10), type = "box"). |
a data frame of partial volume estimates and individual segmentation maps.
Return odd numbered dynamic scans starting from 1 (1,3,5...).
get_odd_dyns(mrs_data)get_odd_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
dynamic MRS data containing odd numbered scans.
Extract the reference component from an mrs_data object.
get_ref(mrs_data)get_ref(mrs_data)
mrs_data |
MRS data. |
reference component.
Get the indices of data points lying between two values (end > x > start).
get_seg_ind(scale, start, end)get_seg_ind(scale, start, end)
scale |
full list of values. |
start |
smallest value in the subset. |
end |
largest value in the subset. |
set of indices.
Return the second half of a dynamic series.
get_sh_dyns(mrs_data)get_sh_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
second half of the dynamic series.
Return a single slice from a larger MRSI dataset.
get_slice(mrs_data, z_pos)get_slice(mrs_data, z_pos)
mrs_data |
MRSI data. |
z_pos |
the z index to extract. |
MRS data.
Return the spin number for a given nucleus.
get_spin_num(nucleus)get_spin_num(nucleus)
nucleus |
nucleus name, eg "1H". |
spin number.
Extract a subset of MRS data.
get_subset( mrs_data, x_set = NULL, y_set = NULL, z_set = NULL, dyn_set = NULL, coil_set = NULL, fd_set = NULL, td_set = NULL )get_subset( mrs_data, x_set = NULL, y_set = NULL, z_set = NULL, dyn_set = NULL, coil_set = NULL, fd_set = NULL, td_set = NULL )
mrs_data |
MRS data object. |
x_set |
x indices to include in the output (default all). |
y_set |
y indices to include in the output (default all). |
z_set |
z indices to include in the output (default all). |
dyn_set |
dynamic indices to include in the output (default all). |
coil_set |
coil indices to include in the output (default all). |
fd_set |
frequency domain data indices to include in the output (default all). |
td_set |
time-domain indices to include in the output (default all). |
selected subset of MRS data.
mrs_data object.Generate a SVS acquisition volume from an mrs_data object.
get_svs_voi(mrs_data, target_mri)get_svs_voi(mrs_data, target_mri)
mrs_data |
MRS data. |
target_mri |
optional image data to match the intended volume space. |
volume data as a nifti object.
Return the last scans of a dynamic series.
get_tail_dyns(mrs_data, n = 1)get_tail_dyns(mrs_data, n = 1)
mrs_data |
dynamic MRS data. |
n |
the number of dynamic scans to return. |
last scans of a dynamic series.
Return an array of amplitudes derived from fitting the initial points in the time domain and extrapolating back to t=0.
get_td_amp(mrs_data, nstart = 10, nend = 50, method = "poly")get_td_amp(mrs_data, nstart = 10, nend = 50, method = "poly")
mrs_data |
MRS data. |
nstart |
first data point to fit. |
nend |
last data point to fit. |
method |
method for measuring the amplitude, one of "poly", spline" or exp". |
array of amplitudes.
Print the command to run the TARQUIN command-line program.
get_tqn_cmd()get_tqn_cmd()
mol_parameters object for a simple spin system with one
resonance.Generate a mol_parameters object for a simple spin system with one
resonance.
get_uncoupled_mol( name, chem_shift, nucleus, scale_factor, lw, lg, full_name = NULL )get_uncoupled_mol( name, chem_shift, nucleus, scale_factor, lw, lg, full_name = NULL )
name |
abbreviated name of the molecule. |
chem_shift |
chemical shift of the resonance (PPM). |
nucleus |
nucleus (1H, 31P...). |
scale_factor |
multiplicative scaling factor. Note, this value can be made complex to adjust the phase of the resonance. |
lw |
linewidth in Hz. |
lg |
Lorentz-Gauss lineshape parameter (between 0 and 1). |
full_name |
long name of the molecule (optional). |
mol_parameters object.
Calculate the centre of gravity for an image containing 0 and 1's.
get_voi_cog(voi)get_voi_cog(voi)
voi |
nifti object. |
triplet of x,y,z coordinates.
Return the white matter, gray matter and CSF composition of a volume.
get_voi_seg(voi, mri_seg)get_voi_seg(voi, mri_seg)
voi |
volume data as a nifti object. |
mri_seg |
segmented brain volume as a nifti object. |
a vector of partial volumes expressed as percentages.
Return the white matter, gray matter and CSF composition of a volume.
get_voi_seg_psf(psf, mri_seg)get_voi_seg_psf(psf, mri_seg)
psf |
volume data as a nifti object. |
mri_seg |
segmented brain volume as a nifti object. |
a vector of partial volumes expressed as percentages.
Return a single voxel from a larger mrs dataset.
get_voxel(mrs_data, x_pos = 1, y_pos = 1, z_pos = 1, dyn = 1, coil = 1)get_voxel(mrs_data, x_pos = 1, y_pos = 1, z_pos = 1, dyn = 1, coil = 1)
mrs_data |
MRS data. |
x_pos |
the x index to plot. |
y_pos |
the y index to plot. |
z_pos |
the z index to plot. |
dyn |
the dynamic index to plot. |
coil |
the coil element number to plot. |
MRS data.
Perform a GLM analysis of dynamic MRS data in the spectral domain.
glm_spec(mrs_data, regressor_df, full_output = FALSE)glm_spec(mrs_data, regressor_df, full_output = FALSE)
mrs_data |
single-voxel dynamics MRS data. |
regressor_df |
a data frame containing temporal regressors to be applied to each spectral datapoint. |
full_output |
append mrs_data and regressor_df to the output list. |
list of statistical results.
Perform first-level spectral GLM analysis of an fMRS dataset.
glm_spec_fmrs_fl( regressor_df, analysis_dir = "spant_analysis", exclude_labels = NULL, labels = NULL, xlim = c(4, 0.2), vline = c(1.35, 1.28, 2.35, 2.29), return_results = FALSE )glm_spec_fmrs_fl( regressor_df, analysis_dir = "spant_analysis", exclude_labels = NULL, labels = NULL, xlim = c(4, 0.2), vline = c(1.35, 1.28, 2.35, 2.29), return_results = FALSE )
regressor_df |
a data frame containing temporal regressors to be applied to each spectral datapoint. |
analysis_dir |
directory containing preprocessed data generated by the preproc_svs_dataset function. |
exclude_labels |
vector of labels of scans to exclude, eg poor quality data. |
labels |
labels to describe each data set. |
xlim |
spectral range to include in the analysis. |
vline |
vertical lines to add to the plot. |
return_results |
function will return key outputs, defaults to FALSE. |
Perform group-level spectral GLM analysis of an fMRS dataset.
glm_spec_fmrs_group( regressor_df, analysis_dir = "spant_analysis", exclude_labels = NULL, labels = NULL )glm_spec_fmrs_group( regressor_df, analysis_dir = "spant_analysis", exclude_labels = NULL, labels = NULL )
regressor_df |
a data frame containing temporal regressors to be applied to each spectral datapoint. |
analysis_dir |
directory containing preprocessed data generated by the preproc_svs_dataset function. |
exclude_labels |
vector of labels of scans to exclude, eg poor quality data. |
labels |
labels to describe each data set. |
Test a group-level spectral GLM linear hypothesis.
glm_spec_group_linhyp(hmat, analysis_dir = "spant_analysis")glm_spec_group_linhyp(hmat, analysis_dir = "spant_analysis")
hmat |
linear hypothesis matrix. |
analysis_dir |
directory containing preprocessed data generated by the preproc_svs_dataset function. |
Grid shift MRSI data in the x/y dimension.
grid_shift_xy(mrs_data, x_shift, y_shift)grid_shift_xy(mrs_data, x_shift, y_shift)
mrs_data |
MRSI data in the spatial domain. |
x_shift |
shift to apply in the x-direction in units of voxels. |
y_shift |
shift to apply in the y-direction in units of voxels. |
shifted data.
Arrange spectral plots in a grid.
gridplot(x, ...)gridplot(x, ...)
x |
object for plotting. |
... |
arguments to be passed to methods. |
Arrange spectral plots in a grid.
## S3 method for class 'mrs_data' gridplot( x, rows = NA, cols = NA, mar = c(0, 0, 0, 0), oma = c(3.5, 1, 1, 1), bty = "o", restore_def_par = TRUE, ... )## S3 method for class 'mrs_data' gridplot( x, rows = NA, cols = NA, mar = c(0, 0, 0, 0), oma = c(3.5, 1, 1, 1), bty = "o", restore_def_par = TRUE, ... )
x |
object of class mrs_data. |
rows |
number of grid rows. |
cols |
number of grid columns. |
mar |
option to adjust the plot margins. See ?par. |
oma |
outer margin area. |
bty |
option to draw a box around the plot. See ?par. |
restore_def_par |
restore default plotting par values after the plot has been made. |
... |
other arguments to pass to the plot method. |
HSVD method as described in: Barkhuijsen H, de Beer R, van Ormondt D. Improved algorithm for noniterative and timedomain model fitting to exponentially damped magnetic resonance signals. J Magn Reson 1987;73:553-557.
hsvd(mrs_data, comps = 40, irlba = TRUE, max_damp = 10)hsvd(mrs_data, comps = 40, irlba = TRUE, max_damp = 10)
mrs_data |
mrs_data object to be decomposed. |
comps |
number of Lorentzian components to use for modelling. |
irlba |
option to use irlba SVD (logical). |
max_damp |
maximum allowable damping factor. |
basis matrix and signal table.
HSVD based signal filter described in: Barkhuijsen H, de Beer R, van Ormondt D. Improved algorithm for noniterative and timedomain model fitting to exponentially damped magnetic resonance signals. J Magn Reson 1987;73:553-557.
hsvd_filt( mrs_data, xlim = c(-30, 30), comps = 40, irlba = TRUE, max_damp = 10, scale = "hz", return_model = FALSE )hsvd_filt( mrs_data, xlim = c(-30, 30), comps = 40, irlba = TRUE, max_damp = 10, scale = "hz", return_model = FALSE )
mrs_data |
MRS data to be filtered. |
xlim |
frequency range to filter, default units are Hz which can be changed to ppm using the "scale" argument. |
comps |
number of Lorentzian components to use for modelling. |
irlba |
option to use irlba SVD (logical). |
max_damp |
maximum allowable damping factor. |
scale |
either "hz" or "ppm" to set the frequency units of xlim. |
return_model |
by default the filtered spectrum is returned. Set return_model to TRUE to return the HSVD model of the data. |
filtered data or model depending on the return_model argument.
HSVD method as described in: Barkhuijsen H, de Beer R, van Ormondt D. Improved algorithm for noniterative and timedomain model fitting to exponentially damped magnetic resonance signals. J Magn Reson 1987;73:553-557.
hsvd_vec(y, fs, comps = 40, irlba = TRUE, max_damp = 0)hsvd_vec(y, fs, comps = 40, irlba = TRUE, max_damp = 0)
y |
time domain signal to be filtered as a vector. |
fs |
sampling frequency of y. |
comps |
number of Lorentzian components to use for modelling. |
irlba |
option to use irlba SVD (logical). |
max_damp |
maximum allowable damping factor. Default value of 0 ensures resultant model is damped. |
basis matrix and signal table.
Return the frequency scale of an MRS dataset in Hz.
hz(mrs_data, fs = NULL, N = NULL)hz(mrs_data, fs = NULL, N = NULL)
mrs_data |
MRS data. |
fs |
sampling frequency in Hz. |
N |
number of data points in the spectral dimension. |
frequency scale.
Perform an iffshift and ifft on a vector.
ift_shift(vec_in)ift_shift(vec_in)
vec_in |
vector input. |
output vector.
Perform an ifft and ifftshift on a matrix with each column replaced by its shifted ifft.
ift_shift_mat(mat_in)ift_shift_mat(mat_in)
mat_in |
matrix input. |
output matrix.
Apply Im operator to an MRS dataset.
## S3 method for class 'mrs_data' Im(z)## S3 method for class 'mrs_data' Im(z)
z |
MRS data. |
MRS data following Im operator.
Image plot method for objects of class mrs_data.
## S3 method for class 'mrs_data' image( x, xlim = NULL, mode = "re", col = NULL, plot_dim = NULL, x_pos = NULL, y_pos = NULL, z_pos = NULL, dyn = 1, coil = 1, restore_def_par = TRUE, y_ticks = NULL, hline = NULL, hline_lty = 2, hline_col = "white", vline = NULL, vline_lty = 2, vline_col = "white", legend = FALSE, ... )## S3 method for class 'mrs_data' image( x, xlim = NULL, mode = "re", col = NULL, plot_dim = NULL, x_pos = NULL, y_pos = NULL, z_pos = NULL, dyn = 1, coil = 1, restore_def_par = TRUE, y_ticks = NULL, hline = NULL, hline_lty = 2, hline_col = "white", vline = NULL, vline_lty = 2, vline_col = "white", legend = FALSE, ... )
x |
object of class mrs_data. |
xlim |
the range of values to display on the x-axis, eg xlim = c(4,1). |
mode |
representation of the complex numbers to be plotted, can be one of: "re", "im", "mod" or "arg". |
col |
Colour map to use, defaults to viridis. |
plot_dim |
the dimension to display on the y-axis, can be one of: "dyn", "time_sec", x", "y", "z", "coil" or NULL. If NULL (the default) all spectra are collapsed into the dynamic dimension and displayed. |
x_pos |
the x index to plot. |
y_pos |
the y index to plot. |
z_pos |
the z index to plot. |
dyn |
the dynamic index to plot. |
coil |
the coil element number to plot. |
restore_def_par |
restore default plotting par values after the plot has been made. |
y_ticks |
a vector of indices specifying where to place additional red tick marks. |
hline |
add a horizontal line at the specified value. |
hline_lty |
linetype for the horizontal line. |
hline_col |
colour for the horizontal line. |
vline |
add a vertical line at the specified value. |
vline_lty |
linetype for the vertical line. |
vline_col |
colour for the vertical line. |
legend |
add a colour bar to the plot using the imagePlot function from the fields package. |
... |
other arguments to pass to the plot method. |
Transform 2D MRSI data to k-space in the x-y direction.
img2kspace_xy(mrs_data)img2kspace_xy(mrs_data)
mrs_data |
2D MRSI data. |
k-space data.
Complex rounding function taken from complexplus package to reduce the number of spant dependencies.
Imzap(x, tol = 1e-06)Imzap(x, tol = 1e-06)
x |
a scalar or vector, real or complex. |
tol |
a tolerance, 10^-6 by default. Prevents possible numerical problems. Can be set to 0 if desired. |
Install the spant command-line interface scripts to a system path.
install_cli(path = NULL)install_cli(path = NULL)
path |
optional path to install the scripts. Defaults to : "/usr/local/bin". |
See spec_op function for a more complete set of spectral operations.
int_spec(mrs_data, xlim = NULL, freq_scale = "ppm", mode = "re")int_spec(mrs_data, xlim = NULL, freq_scale = "ppm", mode = "re")
mrs_data |
MRS data. |
xlim |
spectral range to be integrated (defaults to full range). |
freq_scale |
units of xlim, can be : "ppm", "hz" or "points". |
mode |
spectral mode, can be : "re", "im", "mod" or "cplx". |
an array of integral values.
Interleave the first and second half of a dynamic series.
interleave_dyns(mrs_data)interleave_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
interleaved data.
Invert even numbered dynamic scans starting from 1 (2,4,6...).
inv_even_dyns(mrs_data)inv_even_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
dynamic MRS data with inverted even numbered scans.
Invert odd numbered dynamic scans starting from 1 (1,3,5...).
inv_odd_dyns(mrs_data)inv_odd_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
dynamic MRS data with inverted odd numbered scans.
Check if the chemical shift dimension of an MRS data object is in the frequency domain.
is_fd(mrs_data)is_fd(mrs_data)
mrs_data |
MRS data. |
logical value.
Check if an object is defined, which is the same as being not NULL.
is.def(x)is.def(x)
x |
object to test for being NULL. |
logical value.
Transform 2D MRSI data from k-space to image space in the x-y direction.
kspace2img_xy(mrs_data)kspace2img_xy(mrs_data)
mrs_data |
2D MRSI data. |
MRSI data in image space.
Perform l2 regularisation artefact suppression using the method proposed by Bilgic et al. JMRI 40(1):181-91 2014.
l2_reg( mrs_data, thresh = 0.05, b = 1e-11, A = NA, xlim = NA, thresh_xlim = NULL, A_append = NULL, ret_norms = FALSE )l2_reg( mrs_data, thresh = 0.05, b = 1e-11, A = NA, xlim = NA, thresh_xlim = NULL, A_append = NULL, ret_norms = FALSE )
mrs_data |
input data for artefact suppression. |
thresh |
threshold parameter to extract lipid signals from mrs_data based on the spectral integration of the thresh_xlim region in magnitude mode. |
b |
regularisation parameter. |
A |
set of spectra containing the artefact basis signals. The thresh parameter is ignored when A is specified. |
xlim |
spectral limits in ppm to restrict the reconstruction range. Defaults to the full spectral width. |
thresh_xlim |
spectral limits in ppm to integrate for the threshold map. |
A_append |
additional spectra to append to the A basis. |
ret_norms |
return the residual norm and solution norms. |
l2 reconstructed mrs_data object.
Apply line-broadening (apodisation) to MRS data or basis object.
lb(x, lb, lg = 1) ## S3 method for class 'list' lb(x, lb, lg = 1) ## S3 method for class 'mrs_data' lb(x, lb, lg = 1) ## S3 method for class 'basis_set' lb(x, lb, lg = 1)lb(x, lb, lg = 1) ## S3 method for class 'list' lb(x, lb, lg = 1) ## S3 method for class 'mrs_data' lb(x, lb, lg = 1) ## S3 method for class 'basis_set' lb(x, lb, lg = 1)
x |
input mrs_data or basis_set object. |
lb |
amount of line-broadening in Hz. |
lg |
Lorentz-Gauss lineshape parameter (between 0 and 1). |
line-broadened data.
Correct linear frequency drift.
lofdc( mrs_data, max_hz_s = 0.1, tr = NULL, ret_corr_only = TRUE, outlier_thresh = 3, xlim = c(4, 0.5), order = 1 )lofdc( mrs_data, max_hz_s = 0.1, tr = NULL, ret_corr_only = TRUE, outlier_thresh = 3, xlim = c(4, 0.5), order = 1 )
mrs_data |
MRS data to be corrected. |
max_hz_s |
the maximum drift rate to search over. |
tr |
mrs_data repetition time. |
ret_corr_only |
return the corrected mrs_data object only. |
outlier_thresh |
threshold to remove outliers. |
xlim |
spectral width (in ppm) to evaluate outliers. |
order |
correction order. |
drift corrected mrs_data object.
Covert a linewidth in Hz to an equivalent alpha value in the time-domain ie: x * exp(-t * alpha).
lw2alpha(lw)lw2alpha(lw)
lw |
linewidth in Hz. |
beta damping value.
Covert a linewidth in Hz to an equivalent beta value in the time-domain ie: x * exp(-t * t * beta).
lw2beta(lw)lw2beta(lw)
lw |
linewidth in Hz. |
beta damping value.
Make a basis-set object from a directory containing LCModel formatted RAW files.
make_basis_from_raw(dir_path, ft, fs, ref)make_basis_from_raw(dir_path, ft, fs, ref)
dir_path |
path to the directory containing LCModel RAW files. One file per signal. |
ft |
transmitter frequency in Hz. |
fs |
sampling frequency in Hz. |
ref |
reference value for ppm scale. |
a basis-set object.
Mask an MRS dataset in the dynamic dimension.
mask_dyns(mrs_data, mask)mask_dyns(mrs_data, mask)
mrs_data |
MRS data object. |
mask |
vector of boolean values specifying the dynamics to mask, where a value of TRUE indicates the spectrum should be removed. |
masked dataset.
Mask fit result spectra depending on a vector of bool values.
mask_fit_res(fit_result, mask_vec, amps_only = FALSE)mask_fit_res(fit_result, mask_vec, amps_only = FALSE)
fit_result |
fit result object to be masked. |
mask_vec |
a Boolean vector with the same number of rows as there are rows in the results table. |
amps_only |
only mask the amplitude and associated error estimate columns. |
a masked fit result object.
Mask an MRSI dataset in the x-y direction
mask_xy(mrs_data, x_dim, y_dim)mask_xy(mrs_data, x_dim, y_dim)
mrs_data |
MRS data object. |
x_dim |
x dimension output length. |
y_dim |
y dimension output length. |
masked MRS data.
Mask the four corners of an MRSI dataset in the x-y plane.
mask_xy_corners(mrs_data)mask_xy_corners(mrs_data)
mrs_data |
MRS data object. |
masked MRS data.
Mask the voxels outside an elliptical region spanning the MRSI dataset in the x-y plane.
mask_xy_ellipse(mrs_data)mask_xy_ellipse(mrs_data)
mrs_data |
MRS data object. |
masked MRS data.
Mask a 2D MRSI dataset in the x-y dimension.
mask_xy_mat(mrs_data, mask, value = NA)mask_xy_mat(mrs_data, mask, value = NA)
mrs_data |
MRS data object. |
mask |
matrix of boolean values specifying the voxels to mask, where a value of TRUE indicates the voxel should be removed. |
value |
the value to set masked data to (usually NA or 0). |
masked dataset.
Convert a matrix (with spectral points in the column dimension and dynamics in the row dimensions) into a mrs_data object.
mat2mrs_data( mat, mrs_data = NULL, fs = NULL, ft = NULL, ref = NULL, nuc = NULL, fd = FALSE )mat2mrs_data( mat, mrs_data = NULL, fs = NULL, ft = NULL, ref = NULL, nuc = NULL, fd = FALSE )
mat |
data matrix. |
mrs_data |
example data to copy acquisition parameters from. |
fs |
sampling frequency in Hz. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
nuc |
resonant nucleus. |
fd |
flag to indicate if the matrix is in the frequency domain (logical). |
mrs_data object.
Matrix exponential function taken from complexplus package to reduce the number of spant dependencies.
matexp(x)matexp(x)
x |
a square complex matrix. |
the matrix exponential of x.
Apply the max operator to an MRS dataset.
max_mrs(mrs_data)max_mrs(mrs_data)
mrs_data |
MRS data. |
MRS data following max operator.
Apply the max operator to an interpolated MRS dataset.
max_mrs_interp(mrs_data, interp_f = 4)max_mrs_interp(mrs_data, interp_f = 4)
mrs_data |
MRS data. |
interp_f |
interpolation factor. |
Array of maximum values (real only).
Calculate the mean of adjacent dynamic scans.
mean_dyn_blocks(mrs_data, block_size)mean_dyn_blocks(mrs_data, block_size)
mrs_data |
dynamic MRS data. |
block_size |
number of adjacent dynamics scans to average over. |
dynamic data averaged in blocks.
Calculate the pairwise means across a dynamic data set.
mean_dyn_pairs(mrs_data)mean_dyn_pairs(mrs_data)
mrs_data |
dynamic MRS data. |
mean dynamic data of adjacent dynamic pairs.
Calculate the mean dynamic data.
mean_dyns(mrs_data, subset = NULL)mean_dyns(mrs_data, subset = NULL)
mrs_data |
dynamic MRS data. |
subset |
vector containing indices to the dynamic scans to be averaged. |
mean dynamic data.
Return the mean of a list of mrs_data objects.
mean_mrs_list(mrs_list)mean_mrs_list(mrs_list)
mrs_list |
list of mrs_data objects. |
mean mrs_data object.
Calculate the mean of adjacent blocks in a vector.
mean_vec_blocks(x, block_size)mean_vec_blocks(x, block_size)
x |
input vector. |
block_size |
number of adjacent elements to average over. |
vector data averaged in blocks.
Calculate the mean spectrum from an mrs_data object.
## S3 method for class 'list' mean(x, ...)## S3 method for class 'list' mean(x, ...)
x |
object of class mrs_data. |
... |
other arguments to pass to the colMeans function. |
mean mrs_data object.
Calculate the mean spectrum from an mrs_data object.
## S3 method for class 'mrs_data' mean(x, ...)## S3 method for class 'mrs_data' mean(x, ...)
x |
object of class mrs_data. |
... |
other arguments to pass to the colMeans function. |
mean mrs_data object.
Calculate the median dynamic data.
median_dyns(mrs_data)median_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
median dynamic data.
Apply the Modulus operator to the time-domain MRS signal.
mod_td(mrs_data)mod_td(mrs_data)
mrs_data |
MRS data input. |
time-domain modulus of input.
Apply Mod operator to an MRS dataset.
## S3 method for class 'mrs_data' Mod(z)## S3 method for class 'mrs_data' Mod(z)
z |
MRS data. |
MRS data following Mod operator.
Convert an mrs_data object to basis object - where basis signals are spread across the dynamic dimension in the MRS data.
mrs_data2basis(mrs_data, names)mrs_data2basis(mrs_data, names)
mrs_data |
mrs_data object with basis signals spread across the dynamic dimension. |
names |
list of names corresponding to basis signals. |
basis set object.
Create a BIDS file structure from a vector of MRS data paths or list of mrs_data objects.
mrs_data2bids( mrs_data, output_dir, suffix = NULL, sub = NULL, ses = NULL, task = NULL, acq = NULL, nuc = NULL, voi = NULL, rec = NULL, run = NULL, echo = NULL, inv = NULL, skip_existing = TRUE )mrs_data2bids( mrs_data, output_dir, suffix = NULL, sub = NULL, ses = NULL, task = NULL, acq = NULL, nuc = NULL, voi = NULL, rec = NULL, run = NULL, echo = NULL, inv = NULL, skip_existing = TRUE )
mrs_data |
vector of MRS data paths or list of mrs_data objects. |
output_dir |
the base directory to create the BIDS structure. |
suffix |
optional vector of file suffixes. Default behaviour is to automatically determine these from the input data, however it is recommended that they are specified to allow more efficient skipping of existing data. |
sub |
optional vector of subject labels. If not specified, these will be automatically generated as a series of increasing zero-padded integer values corresponding to the mrs_data input indices. |
ses |
optional vector of session labels. |
task |
optional vector of task labels. |
acq |
optional vector of acquisition labels. |
nuc |
optional vector of nucleus labels. |
voi |
optional vector of volume of interest labels. |
rec |
optional vector of reconstruction labels. |
run |
optional vector of run indices. |
echo |
optional vector of echo time indices. |
inv |
optional vector of inversion indices. |
skip_existing |
skip any data files that have already been converted. Defaults to TRUE, set to FALSE to force an overwrite of any existing data files. |
Convert mrs_data object to a matrix, with spectral points in the column dimension and dynamics in the row dimension.
mrs_data2mat(mrs_data, collapse = TRUE)mrs_data2mat(mrs_data, collapse = TRUE)
mrs_data |
MRS data object or list of MRS data objects. |
collapse |
collapse all other dimensions along the dynamic dimension, eg a 16x16 MRSI grid would be first collapsed across 256 dynamic scans. |
MRS data matrix.
Convert mrs_data object to a matrix, with spectral points in the column dimension and dynamics in the row dimension.
mrs_data2spec_mat(mrs_data, collapse = TRUE)mrs_data2spec_mat(mrs_data, collapse = TRUE)
mrs_data |
MRS data object or list of MRS data objects. |
collapse |
collapse all other dimensions along the dynamic dimension, eg a 16x16 MRSI grid would be first collapsed across 256 dynamic scans. |
MRS data matrix.
Convert mrs_data object to a vector.
mrs_data2vec(mrs_data, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1)mrs_data2vec(mrs_data, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1)
mrs_data |
MRS data object. |
dyn |
dynamic index. |
x_pos |
x index. |
y_pos |
y index. |
z_pos |
z index. |
coil |
coil element index. |
MRS data vector.
Perform a fftshift on a matrix, with each column replaced by its shifted result.
mvfftshift(x)mvfftshift(x)
x |
matrix input. |
output matrix.
Perform an ifftshift on a matrix, with each column replaced by its shifted result.
mvifftshift(x)mvifftshift(x)
x |
matrix input. |
output matrix.
Print fit coordinates from a single index.
n2coord(n, fit_res)n2coord(n, fit_res)
n |
fit index. |
fit_res |
|
Return the total number of coil elements in an MRS dataset.
Ncoils(mrs_data)Ncoils(mrs_data)
mrs_data |
MRS data. |
Return the total number of dynamic scans in an MRS dataset.
Ndyns(mrs_data)Ndyns(mrs_data)
mrs_data |
MRS data. |
Flip the x data dimension order of a nifti image. This corresponds to flipping MRI data in the left-right direction, assuming the data in save in neurological format (can check with fslorient program).
nifti_flip_lr(x)nifti_flip_lr(x)
x |
nifti object to be processed. |
nifti object with reversed x data direction.
Return the number of data points in an MRS dataset.
Npts(mrs_data)Npts(mrs_data)
mrs_data |
MRS data. |
number of data points.
Return the total number of spectra in an MRS dataset.
Nspec(mrs_data)Nspec(mrs_data)
mrs_data |
MRS data. |
Return the total number of acquired transients for an MRS dataset.
Ntrans(mrs_data)Ntrans(mrs_data)
mrs_data |
MRS data. |
Return the total number of x locations in an MRS dataset.
Nx(mrs_data)Nx(mrs_data)
mrs_data |
MRS data. |
Return the total number of y locations in an MRS dataset.
Ny(mrs_data)Ny(mrs_data)
mrs_data |
MRS data. |
Return the total number of z locations in an MRS dataset.
Nz(mrs_data)Nz(mrs_data)
mrs_data |
MRS data. |
Export a one-page pdf of a single fit result
one_page_pdf(fit_res, pdf_out_path, title = NULL)one_page_pdf(fit_res, pdf_out_path, title = NULL)
fit_res |
|
pdf_out_path |
path to the exported pdf file. |
title |
output title. |
Display an orthographic projection plot of a nifti object.
ortho3( underlay, overlay = NULL, xyz = NULL, zlim = NULL, zlim_ol = NULL, alpha = 0.7, col_ol = viridisLite::viridis(64), orient_lab = TRUE, rescale = 1, crosshairs = TRUE, ch_lwd = 1, colourbar = TRUE, bg = "black", mar = c(0, 0, 0, 0), smallplot = c(0.63, 0.65, 0.07, 0.42), legend_axis_cex = 0.75 )ortho3( underlay, overlay = NULL, xyz = NULL, zlim = NULL, zlim_ol = NULL, alpha = 0.7, col_ol = viridisLite::viridis(64), orient_lab = TRUE, rescale = 1, crosshairs = TRUE, ch_lwd = 1, colourbar = TRUE, bg = "black", mar = c(0, 0, 0, 0), smallplot = c(0.63, 0.65, 0.07, 0.42), legend_axis_cex = 0.75 )
underlay |
underlay image to be shown in grayscale. |
overlay |
optional overlay image. |
xyz |
x, y, z slice coordinates to display. |
zlim |
underlay intensity limits. |
zlim_ol |
overlay intensity limits. |
alpha |
transparency of overlay. |
col_ol |
colour palette of overlay. |
orient_lab |
display orientation labels (default TRUE). |
rescale |
rescale factor for the underlay and overlay images. |
crosshairs |
display the crosshairs (default TRUE). |
ch_lwd |
crosshair linewidth. |
colourbar |
display a colourbar for the overlay (default TRUE). |
bg |
plot background colour. |
mar |
plot margins. |
smallplot |
smallplot option for positioning the colourbar. |
legend_axis_cex |
font expansion factor for the legend axis text. |
Display an interactive orthographic projection plot of a nifti object.
ortho3_inter( underlay, overlay = NULL, xyz = NULL, zlim = NULL, zlim_ol = NULL, alpha = 0.7, ... )ortho3_inter( underlay, overlay = NULL, xyz = NULL, zlim = NULL, zlim_ol = NULL, alpha = 0.7, ... )
underlay |
underlay image to be shown in grayscale. |
overlay |
optional overlay image. |
xyz |
x, y, z slice coordinates to display. |
zlim |
underlay intensity limits. |
zlim_ol |
overlay intensity limits. |
alpha |
transparency of overlay. |
... |
other options to be passed to the ortho3 function. |
Search for the highest peak in a spectral region and return the frequency, height and FWHM.
peak_info( mrs_data, xlim = c(4, 0.5), interp_f = 4, scale = "ppm", mode = "real" )peak_info( mrs_data, xlim = c(4, 0.5), interp_f = 4, scale = "ppm", mode = "real" )
mrs_data |
an object of class |
xlim |
frequency range (default units of PPM) to search for the highest peak. |
interp_f |
interpolation factor, defaults to 4x. |
scale |
the units to use for the frequency scale, can be one of: "ppm", "hz" or "points". |
mode |
spectral mode, can be : "real", "imag" or "mod". |
list of arrays containing the highest peak frequency, height and FWHM in units of PPM and Hz.
PG method as described in: Haupt CI, Schuff N, Weiner MW, Maudsley AA. Removal of lipid artifacts in 1H spectroscopic imaging by data extrapolation. Magn Reson Med. 1996 May;35(5):678-87. Extrapolation is performed to expand k-space coverage by a factor of 2, with the aim to reduce Gibbs ringing.
pg_extrap_xy( mrs_data, img_mask = NULL, kspace_mask = NULL, intensity_thresh = 0.15, iters = 50 )pg_extrap_xy( mrs_data, img_mask = NULL, kspace_mask = NULL, intensity_thresh = 0.15, iters = 50 )
mrs_data |
MRS data object. |
img_mask |
a boolean matrix of voxels with strong signals to be extrapolated. Must be twice the dimensions of the input data. |
kspace_mask |
a boolean matrix of kspace points that have been sampled. Typically a circle for MRSI, but defaults to the full rectangular area of k-space covered by the input data. Must match the x-y dimensions of the input data. |
intensity_thresh |
used to define img_mask based on the strength of the signal in each voxel. Defaults to intensities greater than 15% of the maximum. Ignored if img_mask is specified as argument. |
iters |
number of iterations to perform. |
extrapolated mrs_data object.
Apply phasing parameters to MRS data.
phase(mrs_data, zero_order, first_order = 0)phase(mrs_data, zero_order, first_order = 0)
mrs_data |
MRS data. |
zero_order |
zero'th order phase term in degrees. |
first_order |
first order (frequency dependent) phase term in ms. |
MRS data with applied phase parameters.
Corrected zero order phase and chemical shift offset in 1H MRS data from the brain.
phase_ref_1h_brain(mrs_data, mean_ref = FALSE, ret_corr_only = TRUE)phase_ref_1h_brain(mrs_data, mean_ref = FALSE, ret_corr_only = TRUE)
mrs_data |
MRS data to be corrected. |
mean_ref |
apply the phase and offset of the mean spectrum to all others. Default is FALSE. |
ret_corr_only |
return the corrected data only. |
corrected MRS data.
Convenience function to plot a baseline estimate with the original data.
plot_bc(orig_data, bc_data, ...)plot_bc(orig_data, bc_data, ...)
orig_data |
the original data. |
bc_data |
the baseline corrected data. |
... |
other arguments to pass to the stackplot function. |
Plot regressors as an image.
plot_reg(regressor_df)plot_reg(regressor_df)
regressor_df |
input regressor data frame. |
Plot a 2D slice from an MRSI fit result object.
plot_slice_fit( fit_res, map, map_denom = NULL, slice = 1, zlim = NULL, interp = 1 )plot_slice_fit( fit_res, map, map_denom = NULL, slice = 1, zlim = NULL, interp = 1 )
fit_res |
|
map |
fit result values to display as a colour map. Can be specified as a character string or array of numeric values. Defaults to "tNAA". |
map_denom |
fit result values to divide the map argument by. Can be specified as a character string (eg "tCr") or array of numeric values. |
slice |
slice to plot in the z direction. |
zlim |
range of values to plot. |
interp |
interpolation factor. |
Plot a 2D slice from an MRSI fit result object.
plot_slice_fit_inter( fit_res, map = NULL, map_denom = NULL, slice = 1, zlim = NULL, interp = 1, xlim = NULL )plot_slice_fit_inter( fit_res, map = NULL, map_denom = NULL, slice = 1, zlim = NULL, interp = 1, xlim = NULL )
fit_res |
|
map |
fit result values to display as a colour map. Can be specified as a character string or array of numeric values. Defaults to "tNAA". |
map_denom |
fit result values to divide the map argument by. Can be specified as a character string (eg "tCr") or array of numeric values. |
slice |
slice to plot in the z direction. |
zlim |
range of values to plot. |
interp |
interpolation factor. |
xlim |
spectral plot limits for the x axis. |
Plot a slice from a 7 dimensional array.
plot_slice_map( data, zlim = NULL, mask_map = NULL, mask_cutoff = 20, interp = 1, slice = 1, dyn = 1, coil = 1, ref = 1, denom = NULL, horizontal = FALSE )plot_slice_map( data, zlim = NULL, mask_map = NULL, mask_cutoff = 20, interp = 1, slice = 1, dyn = 1, coil = 1, ref = 1, denom = NULL, horizontal = FALSE )
data |
7d array of values to be plotted. |
zlim |
smallest and largest values to be plotted. |
mask_map |
matching map with logical values to indicate if the corresponding values should be plotted. |
mask_cutoff |
minimum values to plot (as a percentage of the maximum). |
interp |
map interpolation factor. |
slice |
the slice index to plot. |
dyn |
the dynamic index to plot. |
coil |
the coil element number to plot. |
ref |
reference index to plot. |
denom |
map to use as a denominator. |
horizontal |
display the colourbar horizontally (logical). |
Plot an interactive slice map from a data array where voxels can be selected to display a corresponding spectrum.
plot_slice_map_inter( mrs_data, map = NULL, xlim = NULL, slice = 1, zlim = NULL, mask_map = NULL, denom = NULL, mask_cutoff = 20, interp = 1, mode = "re", y_scale = FALSE, ylim = NULL, coil = 1, fd = TRUE )plot_slice_map_inter( mrs_data, map = NULL, xlim = NULL, slice = 1, zlim = NULL, mask_map = NULL, denom = NULL, mask_cutoff = 20, interp = 1, mode = "re", y_scale = FALSE, ylim = NULL, coil = 1, fd = TRUE )
mrs_data |
spectral data. |
map |
array of values to be plotted, defaults to the integration of the modulus of the full spectral width. |
xlim |
spectral region to plot. |
slice |
the slice index to plot. |
zlim |
smallest and largest values to be plotted. |
mask_map |
matching map with logical values to indicate if the corresponding values should be plotted. |
denom |
map to use as a denominator. |
mask_cutoff |
minimum values to plot (as a percentage of the maximum). |
interp |
map interpolation factor. |
mode |
representation of the complex spectrum to be plotted, can be one of: "re", "im", "mod" or "arg". |
y_scale |
option to display the y-axis values (logical). |
ylim |
intensity range to plot. |
coil |
coil element to plot. |
fd |
display data in the frequency-domain (default), or time-domain (logical). |
Plot the spectral standard deviation.
plot_spec_sd(mrs_data, xlim = NULL, scale_sd = 1.96, ...)plot_spec_sd(mrs_data, xlim = NULL, scale_sd = 1.96, ...)
mrs_data |
MRS data to be plotted. |
xlim |
plotting limits in ppm. |
scale_sd |
scaling factor for the standard deviation trace. |
... |
other arguments passed to the stackplot function. |
Plot a volume as an image overlay.
plot_voi_overlay(mri, voi, export_path = NULL, zlim = NULL, ...)plot_voi_overlay(mri, voi, export_path = NULL, zlim = NULL, ...)
mri |
image data as a nifti object or path to data file. |
voi |
volume data as a nifti object or path to data file. |
export_path |
optional path to save the image in png format. |
zlim |
underlay intensity limits. |
... |
additional arguments to the ortho3 function. |
Plot a volume as an overlay on a segmented brain volume.
plot_voi_overlay_seg(mri_seg, voi, export_path = NULL, ...)plot_voi_overlay_seg(mri_seg, voi, export_path = NULL, ...)
mri_seg |
segmented brain volume as a nifti object. |
voi |
volume data as a nifti object. |
export_path |
optional path to save the image in png format. |
... |
additional arguments to the ortho3 function. |
fit_result.Plot the fitting results of an object of class fit_result.
## S3 method for class 'fit_result' plot( x, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1, xlim = NULL, data_only = FALSE, label = NULL, plot_sigs = NULL, n = NULL, sub_bl = FALSE, mar = NULL, restore_def_par = TRUE, ylim = NULL, y_scale = FALSE, show_grid = TRUE, grid_nx = NULL, grid_ny = NA, invert_fit = FALSE, ... )## S3 method for class 'fit_result' plot( x, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1, xlim = NULL, data_only = FALSE, label = NULL, plot_sigs = NULL, n = NULL, sub_bl = FALSE, mar = NULL, restore_def_par = TRUE, ylim = NULL, y_scale = FALSE, show_grid = TRUE, grid_nx = NULL, grid_ny = NA, invert_fit = FALSE, ... )
x |
fit_result object. |
dyn |
the dynamic index to plot. |
x_pos |
the x index to plot. |
y_pos |
the y index to plot. |
z_pos |
the z index to plot. |
coil |
the coil element number to plot. |
xlim |
the range of values to display on the x-axis, eg xlim = c(4,1). |
data_only |
display only the processed data (logical). |
label |
character string to add to the top left of the plot window. |
plot_sigs |
a character vector of signal names to add to the plot. |
n |
single index element to plot (overrides other indices when given). |
sub_bl |
subtract the baseline from the data and fit (logical). |
mar |
option to adjust the plot margins. See ?par. |
restore_def_par |
restore default plotting par values after the plot has been made. |
ylim |
range of values to display on the y-axis, eg ylim = c(0,10). |
y_scale |
option to display the y-axis values (logical). |
show_grid |
plot gridlines behind the data (logical). Defaults to TRUE. |
grid_nx |
number of cells of the grid in x and y direction. When NULL the grid aligns with the tick marks on the corresponding default axis (i.e., tickmarks as computed by axTicks). When NA, no grid lines are drawn in the corresponding direction. |
grid_ny |
as above. |
invert_fit |
show the fit result "upside-down"/ |
... |
further arguments to plot method. |
Plotting method for objects of class mrs_data.
## S3 method for class 'mrs_data' plot( x, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1, fd = TRUE, x_units = NULL, xlim = NULL, y_scale = FALSE, x_ax = TRUE, mode = "re", lwd = NULL, bty = NULL, label = "", restore_def_par = TRUE, mar = NULL, xaxis_lab = NULL, yaxis_lab = NULL, xat = NULL, xlabs = TRUE, yat = NULL, ylabs = TRUE, show_grid = TRUE, grid_nx = NULL, grid_ny = NA, col = NULL, alpha = NULL, bl_lty = NULL, hline = NULL, hline_lty = 2, hline_col = "red", vline = NULL, vline_lty = 2, vline_col = "red", ... )## S3 method for class 'mrs_data' plot( x, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1, fd = TRUE, x_units = NULL, xlim = NULL, y_scale = FALSE, x_ax = TRUE, mode = "re", lwd = NULL, bty = NULL, label = "", restore_def_par = TRUE, mar = NULL, xaxis_lab = NULL, yaxis_lab = NULL, xat = NULL, xlabs = TRUE, yat = NULL, ylabs = TRUE, show_grid = TRUE, grid_nx = NULL, grid_ny = NA, col = NULL, alpha = NULL, bl_lty = NULL, hline = NULL, hline_lty = 2, hline_col = "red", vline = NULL, vline_lty = 2, vline_col = "red", ... )
x |
object of class mrs_data. |
dyn |
the dynamic index to plot. |
x_pos |
the x index to plot. |
y_pos |
the y index to plot. |
z_pos |
the z index to plot. |
coil |
the coil element number to plot. |
fd |
display data in the frequency-domain (default), or time-domain (logical). |
x_units |
the units to use for the x-axis, can be one of: "ppm", "hz", "points" or "seconds". |
xlim |
the range of values to display on the x-axis, eg xlim = c(4,1). |
y_scale |
option to display the y-axis values (logical). |
x_ax |
option to display the x-axis values (logical). |
mode |
representation of the complex numbers to be plotted, can be one of: "re", "im", "mod" or "arg". |
lwd |
plot linewidth. |
bty |
option to draw a box around the plot. See ?par. |
label |
character string to add to the top left of the plot window. |
restore_def_par |
restore default plotting par values after the plot has been made. |
mar |
option to adjust the plot margins. See ?par. |
xaxis_lab |
x-axis label. |
yaxis_lab |
y-axis label. |
xat |
x-axis tick label values. |
xlabs |
x-axis tick labels. |
yat |
y-axis tick label values. |
ylabs |
y-axis tick labels. |
show_grid |
plot gridlines behind the data (logical). Defaults to TRUE. |
grid_nx |
number of cells of the grid in x and y direction. When NULL the grid aligns with the tick marks on the corresponding default axis (i.e., tickmarks as computed by axTicks). When NA, no grid lines are drawn in the corresponding direction. |
grid_ny |
as above. |
col |
set the line colour, eg col = rgb(0.5, 0.5, 0.5). |
alpha |
set the line transparency, eg alpha = 0.5 is 50% transparency. Overrides any transparency levels set by col. |
bl_lty |
linetype for the y = 0 baseline trace. A default value NULL results in no baseline being plotted. |
hline |
add a horizontal line at the specified value. |
hline_lty |
linetype for the horizontal line. |
hline_col |
colour for the horizontal line. |
vline |
add a vertical line at the specified value. |
vline_lty |
linetype for the vertical line. |
vline_col |
colour for the vertical line. |
... |
other arguments to pass to the plot method. |
Return the ppm scale of an MRS dataset or fit result.
ppm(x, ft = NULL, ref = NULL, fs = NULL, N = NULL) ## S3 method for class 'mrs_data' ppm(x, ft = NULL, ref = NULL, fs = NULL, N = NULL) ## S3 method for class 'fit_result' ppm(x, ft = NULL, ref = NULL, fs = NULL, N = NULL)ppm(x, ft = NULL, ref = NULL, fs = NULL, N = NULL) ## S3 method for class 'mrs_data' ppm(x, ft = NULL, ref = NULL, fs = NULL, N = NULL) ## S3 method for class 'fit_result' ppm(x, ft = NULL, ref = NULL, fs = NULL, N = NULL)
x |
MRS dataset of fit result. |
ft |
transmitter frequency in Hz, does not apply when the object is a fit result. |
ref |
reference value for ppm scale, does not apply when the object is a fit result. |
fs |
sampling frequency in Hz, does not apply when the object is a fit result. |
N |
number of data points in the spectral dimension, does not apply when the object is a fit result. |
ppm scale.
Save function results to file and load on subsequent calls to avoid repeat computation.
precomp(file, fun, ...)precomp(file, fun, ...)
file |
file name to write the results. |
fun |
function to run. |
... |
arguments to be passed to fun. |
Preprocess and perform quality assessment of a single SVS data set.
preproc_svs(path, label = NULL, output_dir = NULL, ref_inds = NULL)preproc_svs(path, label = NULL, output_dir = NULL, ref_inds = NULL)
path |
path to the fMRS data file or IMA directory. |
label |
a label to describe the data set. |
output_dir |
output directory. |
ref_inds |
a vector of 1-based indices for any water reference dynamic scans. |
Preprocess and perform quality assessment of one or more SVS data sets.
preproc_svs_dataset( paths, labels = NULL, output_dir = "spant_analysis", exclude_labels = NULL, overwrite = FALSE, ref_inds = NULL, return_results = FALSE )preproc_svs_dataset( paths, labels = NULL, output_dir = "spant_analysis", exclude_labels = NULL, overwrite = FALSE, ref_inds = NULL, return_results = FALSE )
paths |
paths to the fMRS data file or IMA directory. |
labels |
labels to describe each data set. |
output_dir |
output directory. |
exclude_labels |
vector of labels of scans to exclude, eg poor quality data. |
overwrite |
overwrite saved results, defaults to FALSE. |
ref_inds |
a vector of 1-based indices for any water reference dynamic scans. |
return_results |
function will return key outputs, defaults to FALSE. |
fit_result.Print a summary of an object of class fit_result.
## S3 method for class 'fit_result' print(x, ...)## S3 method for class 'fit_result' print(x, ...)
x |
|
... |
further arguments. |
Print a summary of mrs_data parameters.
## S3 method for class 'mrs_data' print(x, full = FALSE, ...)## S3 method for class 'mrs_data' print(x, full = FALSE, ...)
x |
mrs_data object. |
full |
print all parameters (default FALSE). |
... |
further arguments. |
Get the quantum coherence matrix for a spin system.
qn_states(sys)qn_states(sys)
sys |
spin system object. |
quantum coherence number matrix.
Robust Alignment to a Target Spectrum (RATS).
rats( mrs_data, ref = NULL, xlim = c(4, 0.5), max_shift = 20, p_deg = 2, sp_N = 2, sp_deg = 3, max_t = 0.2, basis_type = "poly", rescale_output = TRUE, phase_corr = TRUE, ret_corr_only = TRUE, zero_freq_shift_t0 = FALSE, remove_freq_outliers = FALSE, freq_outlier_thresh = 3, remove_phase_outliers = FALSE, phase_outlier_thresh = 3, remove_amp_outliers = FALSE, amp_outlier_thresh = 3 )rats( mrs_data, ref = NULL, xlim = c(4, 0.5), max_shift = 20, p_deg = 2, sp_N = 2, sp_deg = 3, max_t = 0.2, basis_type = "poly", rescale_output = TRUE, phase_corr = TRUE, ret_corr_only = TRUE, zero_freq_shift_t0 = FALSE, remove_freq_outliers = FALSE, freq_outlier_thresh = 3, remove_phase_outliers = FALSE, phase_outlier_thresh = 3, remove_amp_outliers = FALSE, amp_outlier_thresh = 3 )
mrs_data |
MRS data to be corrected. |
ref |
optional MRS data to use as a reference, the mean of all dynamics is used if this argument is not supplied. |
xlim |
optional frequency range to perform optimisation, set to NULL to use the full range. |
max_shift |
maximum allowable frequency shift in Hz. |
p_deg |
polynomial degree used for baseline modelling. Negative values disable baseline modelling. |
sp_N |
number of spline functions, note the true number will be sp_N + sp_deg. |
sp_deg |
degree of spline functions. |
max_t |
truncate the FID when longer than max_t to reduce time taken, set to NULL to use the entire FID. |
basis_type |
may be one of "poly" or "spline". |
rescale_output |
rescale the bl_matched_spec and bl output to improve consistency between dynamic scans. |
phase_corr |
apply phase correction (in addition to frequency). TRUE by default. |
ret_corr_only |
return the corrected mrs_data object only. |
zero_freq_shift_t0 |
perform a linear fit to the frequency shifts and set the (linearly modeled) shift to be 0 Hz for the first dynamic scan. |
remove_freq_outliers |
remove dynamics based on their frequency shift. |
freq_outlier_thresh |
threshold to remove frequency outliers. |
remove_phase_outliers |
remove dynamics based on their phase shift. |
phase_outlier_thresh |
threshold to remove phase outliers. |
remove_amp_outliers |
remove dynamics based on their amplitude change. |
amp_outlier_thresh |
threshold to remove amplitude outliers. |
a list containing the corrected data; phase and shift values in units of degrees and Hz respectively.
Apply a weighting to the FID to enhance spectral resolution.
re_weighting(mrs_data, re, alpha)re_weighting(mrs_data, re, alpha)
mrs_data |
data to be enhanced. |
re |
resolution enhancement factor (rising exponential factor). |
alpha |
alpha factor (Gaussian decay) |
resolution enhanced mrs_data.
Apply Re operator to an MRS dataset.
## S3 method for class 'mrs_data' Re(z)## S3 method for class 'mrs_data' Re(z)
z |
MRS data. |
MRS data following Re operator.
Read a basis file in LCModel .basis format.
read_basis(basis_file, ref = def_ref(), sort_basis = TRUE)read_basis(basis_file, ref = def_ref(), sort_basis = TRUE)
basis_file |
path to basis file. |
ref |
assumed ppm reference value. |
sort_basis |
sort the basis set based on signal names. |
basis object.
Read a directory containing Siemens MRS IMA files and combine along the coil dimension. Note that the coil ID is inferred from the sorted file name and should be checked when consistency is required between two directories.
read_ima_coil_dir(dir, extra = NULL, verbose = FALSE)read_ima_coil_dir(dir, extra = NULL, verbose = FALSE)
dir |
data directory path. |
extra |
an optional data frame to provide additional variables for use in subsequent analysis steps, eg id or grouping variables. |
verbose |
output extra information to the console. |
mrs_data object.
Read a directory containing Siemens MRS IMA files and combine along the dynamic dimension. Note that the coil ID is inferred from the sorted file name and should be checked when consistency is required.
read_ima_dyn_dir(dir, extra = NULL, verbose = FALSE)read_ima_dyn_dir(dir, extra = NULL, verbose = FALSE)
dir |
data directory path. |
extra |
an optional data frame to provide additional variables for use in subsequent analysis steps, eg id or grouping variables. |
verbose |
output extra information to the console. |
mrs_data object.
Read an LCModel formatted coord file containing fit information.
read_lcm_coord(coord_f)read_lcm_coord(coord_f)
coord_f |
path to the coord file. |
list containing a table of fit point and results structure containing signal amplitudes, errors and fitting diagnostics.
Read MRS data from the filesystem.
read_mrs( path, format = NULL, ft = NULL, fs = NULL, ref = NULL, n_ref_scans = NULL, full_fid = FALSE, omit_svs_ref_scans = TRUE, verbose = FALSE, extra = NULL, fid_filt_dist = NULL )read_mrs( path, format = NULL, ft = NULL, fs = NULL, ref = NULL, n_ref_scans = NULL, full_fid = FALSE, omit_svs_ref_scans = TRUE, verbose = FALSE, extra = NULL, fid_filt_dist = NULL )
path |
file name or directory containing the MRS data. |
format |
string describing the data format. Must be one of the following : "spar_sdat", "rda", "dicom", "twix", "pfile", "list_data", "paravis", "dpt", "lcm_raw", "rds", "nifti", "varian", "jmrui_txt". If not specified, the format will be guessed from the filename extension, or will be assumed to be a Siemens ima dynamic data if the path is a directory. |
ft |
transmitter frequency in Hz (required for list_data format). |
fs |
sampling frequency in Hz (required for list_data format). |
ref |
reference value for ppm scale (required for list_data format). |
n_ref_scans |
override the number of water reference scans detected in the file header (GE p-file only). |
full_fid |
export all data points, including those before the start of the FID (default = FALSE), TWIX format only. |
omit_svs_ref_scans |
remove any reference scans sometimes saved in SVS twix data (default = TRUE). |
verbose |
print data file information (default = FALSE). |
extra |
an optional data frame to provide additional variables for use in subsequent analysis steps, eg id or grouping variables. |
fid_filt_dist |
indicate if the data has a distorted FID due to a brick-wall filter being used to downsample the data. Default is to auto detect this from the data, but TRUE or FALSE options can be given to override detection. |
MRS data object.
fname <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") mrs_data <- read_mrs(fname) print(mrs_data)fname <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") mrs_data <- read_mrs(fname) print(mrs_data)
Read MRS data using the TARQUIN software package.
read_mrs_tqn(fname, fname_ref = NA, format, id = NA, group = NA)read_mrs_tqn(fname, fname_ref = NA, format, id = NA, group = NA)
fname |
the filename containing the MRS data. |
fname_ref |
a second filename containing reference MRS data. |
format |
format of the MRS data. Can be one of the following: siemens, philips, ge, dcm, dpt, rda, lcm, varian, bruker, jmrui_txt. |
id |
optional ID string. |
group |
optional group string. |
MRS data object.
fname <- system.file("extdata","philips_spar_sdat_WS.SDAT",package="spant") ## Not run: mrs_data <- read_mrs_tqn(fname, format="philips") ## End(Not run)fname <- system.file("extdata","philips_spar_sdat_WS.SDAT",package="spant") ## Not run: mrs_data <- read_mrs_tqn(fname, format="philips") ## End(Not run)
Read an ASCII formatted pulse file.
read_pulse_ascii(fname, deg2rad = TRUE)read_pulse_ascii(fname, deg2rad = TRUE)
fname |
ASCII formatted pulse file path. |
deg2rad |
convert phase values stored in degrees to radians. |
pulse waveform and header.
Read a Bruker formatted pulse file
read_pulse_bruker(fname)read_pulse_bruker(fname)
fname |
Bruker formatted pulse file path. |
pulse waveform and header.
Read a .pta formatted pulse file compatible with Siemens PulseTool.
read_pulse_pta(fname)read_pulse_pta(fname)
fname |
pta formatted pulse file path. |
pulse waveform and header.
Read the text format header found in Siemens IMA and TWIX data files.
read_siemens_txt_hdr(input, version = "vd", verbose = FALSE, offset = 0)read_siemens_txt_hdr(input, version = "vd", verbose = FALSE, offset = 0)
input |
file name to read or raw data. |
version |
software version, can be "vb" or "vd". |
verbose |
print information to the console. |
offset |
offset to begin searching for the text header. |
a list of parameter values
Reader for csv fit results generated by TARQUIN.
read_tqn_fit(fit_f)read_tqn_fit(fit_f)
fit_f |
TARQUIN fit file. |
A data frame of the fit data points.
## Not run: fit <- read_tqn_fit(system.file("extdata","fit.csv",package="spant")) ## End(Not run)## Not run: fit <- read_tqn_fit(system.file("extdata","fit.csv",package="spant")) ## End(Not run)
Reader for csv results generated by TARQUIN.
read_tqn_result(result_f, remove_rcs = TRUE)read_tqn_result(result_f, remove_rcs = TRUE)
result_f |
TARQUIN result file. |
remove_rcs |
omit row, column and slice ids from output. |
list of amplitudes, crlbs and diagnostics.
## Not run: result <- read_tqn_result(system.file("extdata","result.csv",package="spant")) ## End(Not run)## Not run: result <- read_tqn_result(system.file("extdata","result.csv",package="spant")) ## End(Not run)
Reconstruct complex time-domain data from the real part of frequency-domain data.
recon_imag(mrs_data)recon_imag(mrs_data)
mrs_data |
MRS data. |
reconstructed MRS data.
Reconstruct complex time-domain data from the real part of frequency-domain data.
recon_imag_vec(data)recon_imag_vec(data)
data |
data points in the frequency domain. |
reconstructed signal.
Reconstruct 2D MRSI data from a twix file loaded with read_mrs.
recon_twix_2d_mrsi(twix_mrs)recon_twix_2d_mrsi(twix_mrs)
twix_mrs |
raw dynamic data. |
reconstructed data.
Create a rectangular mask stored as a matrix of logical values.
rectangular_mask(xN, yN, x0, y0, xw, yw, angle)rectangular_mask(xN, yN, x0, y0, xw, yw, angle)
xN |
number of pixels in the x dimension. |
yN |
number of pixels in the y dimension. |
x0 |
centre of rectangle in the x direction in units of pixels. |
y0 |
centre of rectangle in the y direction in units of pixels. |
xw |
width in the x direction in units of pixels. |
yw |
width in the y direction in units of pixels. |
angle |
angle of rotation in degrees. |
logical mask matrix with dimensions fov_yN x fov_xN.
Repeat an array over a given dimension.
rep_array_dim(x, rep_dim, n)rep_array_dim(x, rep_dim, n)
x |
array. |
rep_dim |
dimension to extend. |
n |
number of times to repeat. |
extended array.
Replicate a scan in the dynamic dimension.
rep_dyn(mrs_data, times)rep_dyn(mrs_data, times)
mrs_data |
MRS data to be replicated. |
times |
number of times to replicate. |
replicated data object.
Replicate a scan over a given dimension.
rep_mrs( mrs_data, x_rep = 1, y_rep = 1, z_rep = 1, dyn_rep = 1, coil_rep = 1, warn = TRUE )rep_mrs( mrs_data, x_rep = 1, y_rep = 1, z_rep = 1, dyn_rep = 1, coil_rep = 1, warn = TRUE )
mrs_data |
MRS data to be replicated. |
x_rep |
number of x replications. |
y_rep |
number of y replications. |
z_rep |
number of z replications. |
dyn_rep |
number of dynamic replications. |
coil_rep |
number of coil replications. |
warn |
print a warning when the data dimensions do not change. |
replicated data object.
Resample a basis-set to match a mrs_data acquisition.
resample_basis(basis, mrs_data, ref_freq_match = TRUE)resample_basis(basis, mrs_data, ref_freq_match = TRUE)
basis |
the basis to be resampled. |
mrs_data |
the mrs_data to match the number of data points and sampling frequency. |
ref_freq_match |
apply a frequency shift to the basis to match the reference frequency (usually 4.65 or 4.68) of the mrs_data. |
resampled basis set object.
Resample an image to match a target image space.
resample_img(source, target, interp = 3L)resample_img(source, target, interp = 3L)
source |
image data as a nifti object. |
target |
image data as a nifti object. |
interp |
interpolation parameter, see nifyreg.linear definition. |
resampled image data as a nifti object.
Resample a VOI to match a target image space using nearest-neighbour interpolation.
resample_voi(voi, mri)resample_voi(voi, mri)
voi |
volume data as a nifti object. |
mri |
image data as a nifti object. |
volume data as a nifti object.
Reslice a nifti object to match the orientation of mrs data.
reslice_to_mrs(mri, mrs, interp = 3L)reslice_to_mrs(mri, mrs, interp = 3L)
mri |
nifti object to be resliced. |
mrs |
mrs_data object for the target orientation. |
interp |
interpolation parameter, see nifyreg.linear definition. |
resliced imaging data.
Generate mrs_data from a table of single Lorentzian resonances.
reson_table2mrs_data( reson_table, acq_paras = def_acq_paras(), back_extrap_pts = 0 )reson_table2mrs_data( reson_table, acq_paras = def_acq_paras(), back_extrap_pts = 0 )
reson_table |
as produced by the hsvd function. |
acq_paras |
list of acquisition parameters. See |
back_extrap_pts |
number of data points to back extrapolate
|
mrs_data object.
Remove a subset of dynamic scans.
rm_dyns(mrs_data, subset)rm_dyns(mrs_data, subset)
mrs_data |
dynamic MRS data. |
subset |
vector containing indices to the dynamic scans to be removed. |
MRS data without the specified dynamic scans.
See the LCModel manual (section 10.2) on water-scaling for details on the assumptions and relevant references. Use this type of concentration scaling to compare fit results with LCModel and TARQUIN defaults. Otherwise scale_amp_molal_pvc is the preferred method. Note, the LCModel manual (section 1.3) states:
scale_amp_legacy(fit_result, ref_data, w_att = 0.7, w_conc = 35880, ...)scale_amp_legacy(fit_result, ref_data, w_att = 0.7, w_conc = 35880, ...)
fit_result |
a result object generated from fitting. |
ref_data |
water reference MRS data object. |
w_att |
water attenuation factor (default = 0.7). Assumes water T2 of 80ms and a TE = 30 ms. exp(-30ms / 80ms) ~ 0.7. |
w_conc |
assumed water concentration (default = 35880). Default value corresponds to typical white matter. Set to 43300 for gray matter, and 55556 for phantom measurements. |
... |
additional arguments to get_td_amp function. |
"Concentrations should be labelled 'mmol per Kg wet weight'. We use the shorter (incorrect) abbreviation mM. The actual mM is the mmol per Kg wet weight multiplied by the specific gravity of the tissue, typically 1.04 in brain."
a fit_result object with a rescaled results table.
Note, this function assumes the volume contains a homogeneous voxel, eg pure WM, GM or CSF. Also note that in the case of a homogeneous voxel the relative densities of MR-visible water (eg GM=0.78, WM=0.65, and CSF=0.97) cancel out and don't need to be considered. Use scale_amp_molal_pvc for volumes containing multiple compartments. Details of this method can be found in "Use of tissue water as a concentration reference for proton spectroscopic imaging" by Gasparovic et al MRM 2006 55(6):1219-26.
scale_amp_molal( fit_result, ref_data, te, tr, water_t1, water_t2, metab_t1, metab_t2, ... )scale_amp_molal( fit_result, ref_data, te, tr, water_t1, water_t2, metab_t1, metab_t2, ... )
fit_result |
result object generated from fitting. |
ref_data |
water reference MRS data object. |
te |
the MRS TE in seconds. |
tr |
the MRS TR in seconds. |
water_t1 |
assumed water T1 value. |
water_t2 |
assumed water T2 value. |
metab_t1 |
assumed metabolite T1 value. |
metab_t2 |
assumed metabolite T2 value. |
... |
additional arguments to get_td_amp function. |
A fit_result object with a rescaled results table.
Details of this method can be found in "Use of tissue water as a concentration reference for proton spectroscopic imaging" by Gasparovic et al MRM 2006 55(6):1219-26. 1.5 Tesla relaxation assumptions are taken from this paper. For 3 Tesla data, relaxation assumptions are taken from "NMR relaxation times in the human brain at 3.0 Tesla" by Wansapura et al J Magn Reson Imaging 1999 9(4):531-8.
scale_amp_molal_pvc(fit_result, ref_data, p_vols, te, tr, ...)scale_amp_molal_pvc(fit_result, ref_data, p_vols, te, tr, ...)
fit_result |
result object generated from fitting. |
ref_data |
water reference MRS data object. |
p_vols |
a numeric vector of partial volumes expressed as percentages. For example, a voxel containing 100% white matter tissue would use : p_vols = c(WM = 100, GM = 0, CSF = 0). |
te |
the MRS TE in seconds. |
tr |
the MRS TR in seconds. |
... |
additional arguments to get_td_amp function. |
A fit_result object with a rescaled results table.
See the LCModel manual (section 10.2) on water-scaling for details on the assumptions and relevant references. Use this type of concentration scaling to compare fit results with LCModel and TARQUIN defaults. Otherwise scale_amp_molal_pvc is generally the preferred method.
scale_amp_molar(fit_result, ref_data, w_att = 0.7, w_conc = 35880, ...)scale_amp_molar(fit_result, ref_data, w_att = 0.7, w_conc = 35880, ...)
fit_result |
a result object generated from fitting. |
ref_data |
water reference MRS data object. |
w_att |
water attenuation factor (default = 0.7). Assumes water T2 of 80ms and a TE = 30 ms. exp(-30ms / 80ms) ~ 0.7. |
w_conc |
assumed water concentration (default = 35880). Default value corresponds to typical white matter. Set to 43300 for gray matter, and 55556 for phantom measurements. |
... |
additional arguments to get_td_amp function. |
a fit_result object with a rescaled results table.
Convert default LCM/TARQUIN concentration scaling to molal units with partial volume correction.
scale_amp_molar2molal_pvc(fit_result, p_vols, te, tr)scale_amp_molar2molal_pvc(fit_result, p_vols, te, tr)
fit_result |
a |
p_vols |
a numeric vector of partial volumes expressed as percentages. For example, a voxel containing 100% white matter tissue would use : p_vols = c(WM = 100, GM = 0, CSF = 0). |
te |
the MRS TE. |
tr |
the MRS TR. |
a fit_result object with a rescaled results table.
Scale fitted amplitudes to a ratio of signal amplitude.
scale_amp_ratio(fit_result, name, use_mean_value = FALSE)scale_amp_ratio(fit_result, name, use_mean_value = FALSE)
fit_result |
a result object generated from fitting. |
name |
the signal name to use as a denominator (usually, "tCr" or "tNAA"). |
use_mean_value |
scales the result by the mean of the signal when set to TRUE. |
a fit_result object with a rescaled results table.
Scale fitted amplitudes to a ratio of signal amplitude.
scale_amp_ratio_value(fit_result, value)scale_amp_ratio_value(fit_result, value)
fit_result |
a result object generated from fitting. |
value |
the number use as a denominator. |
a fit_result object with a rescaled results table.
Scale metabolite amplitudes as a ratio to the unsuppressed water amplitude.
scale_amp_water_ratio(fit_result, ref_data, ...)scale_amp_water_ratio(fit_result, ref_data, ...)
fit_result |
a result object generated from fitting. |
ref_data |
a water reference MRS data object. |
... |
additional arguments to get_td_amp function. |
a fit_result object with a rescaled results table.
Scale a basis object by a scalar.
scale_basis_amp(basis, amp)scale_basis_amp(basis, amp)
basis |
basis_set object to be scaled. |
amp |
multiplicative factor with length 1. |
basis_set object multiplied by the amplitude scale factor.
For correct water scaling, spant assumes the time-domain amplitude (t = 0) for a single proton is 0.5. Internally simulated basis-sets will be correctly scaled, however imported basis-sets should be assumed to be un-scaled and this function should be used. Note that the singlet specified should only contain one resonance, and that any additional signals (eg TSP or residual water) will result in incorrect scaling. Therefore, only simulated basis sets are appropriate for use with this function.
scale_basis_from_singlet(basis, name, protons)scale_basis_from_singlet(basis, name, protons)
basis |
basis set to be scaled. |
name |
the name of the singlet to be used as a scaling reference. |
protons |
the number of MRS visible protons contributing to the singlet resonance. |
a scaled basis.
Scale an mrs_data object by a scalar or vector or amplitudes.
scale_mrs_amp(mrs_data, amp)scale_mrs_amp(mrs_data, amp)
mrs_data |
data to be scaled. |
amp |
multiplicative factor, must have length equal to 1 or Nspec(mrs_data). |
mrs_data object multiplied by the amplitude scale factor.
Scale mrs_data to a spectral region.
scale_spec( mrs_data, xlim = NULL, operator = "sum", freq_scale = "ppm", mode = "re", mean_dyns = NULL, ret_scale_factor = FALSE )scale_spec( mrs_data, xlim = NULL, operator = "sum", freq_scale = "ppm", mode = "re", mean_dyns = NULL, ret_scale_factor = FALSE )
mrs_data |
MRS data. |
xlim |
spectral range to be integrated (defaults to full range). |
operator |
can be "sum" (default), "mean", "l2", "max", "min" or "max-min". |
freq_scale |
units of xlim, can be : "ppm", "Hz" or "points". |
mode |
spectral mode, can be : "re", "im", "mod" or "cplx". |
mean_dyns |
mean the dynamic scans before applying the operator. The same scaling value will be applied to each individual dynamic. |
ret_scale_factor |
option to return the scaling factor in addition to the scaled data. |
normalised data.
Calculate the standard deviation spectrum from an mrs_data object.
sd(x, na.rm)sd(x, na.rm)
x |
object of class mrs_data. |
na.rm |
remove NA values. |
sd mrs_data object.
Calculate the standard deviation spectrum from an mrs_data object.
## S3 method for class 'mrs_data' sd(x, na.rm = FALSE)## S3 method for class 'mrs_data' sd(x, na.rm = FALSE)
x |
object of class mrs_data. |
na.rm |
remove NA values. |
sd mrs_data object.
Return a time scale vector to match the FID of an MRS data object.
seconds(mrs_data)seconds(mrs_data)
mrs_data |
MRS data. |
time scale vector in units of seconds.
CPMG style sequence with ideal pulses.
seq_cpmg_ideal(spin_params, ft, ref, TE = 0.03, echoes = 4)seq_cpmg_ideal(spin_params, ft, ref, TE = 0.03, echoes = 4)
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
TE |
echo time in seconds. |
echoes |
number of echoes. |
list of resonance amplitudes and frequencies.
MEGA-PRESS sequence with ideal localisation pulses and Gaussian shaped editing pulse.
seq_mega_press_ideal( spin_params, ft, ref, ed_freq = 1.89, TE1 = 0.015, TE2 = 0.053, BW = 110, steps = 50 )seq_mega_press_ideal( spin_params, ft, ref, ed_freq = 1.89, TE1 = 0.015, TE2 = 0.053, BW = 110, steps = 50 )
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
ed_freq |
editing pulse frequency in ppm. |
TE1 |
TE1 sequence parameter in seconds (TE=TE1+TE2). |
TE2 |
TE2 sequence parameter in seconds. |
BW |
editing pulse bandwidth in Hz. |
steps |
number of hard pulses used to approximate the editing pulse. |
list of resonance amplitudes and frequencies.
PRESS sequence with shaped refocusing pulses.
seq_press_2d_shaped( spin_params, ft, ref, TE1 = 0.01, TE2 = 0.02, pulse_file, pulse_dur, pulse_file_format, refoc_flip_angle = 180, xy_pulse_ppm = NULL, resamp = TRUE, fs_resamp = 1e-04 )seq_press_2d_shaped( spin_params, ft, ref, TE1 = 0.01, TE2 = 0.02, pulse_file, pulse_dur, pulse_file_format, refoc_flip_angle = 180, xy_pulse_ppm = NULL, resamp = TRUE, fs_resamp = 1e-04 )
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
TE1 |
TE1 sequence parameter in seconds (TE=TE1+TE2). |
TE2 |
TE2 sequence parameter in seconds. |
pulse_file |
path to refocusing pulse file. |
pulse_dur |
refocusing pulse duration. |
pulse_file_format |
file format for the refocusing pulse. |
refoc_flip_angle |
refocusing pulse flip angle in degrees (defaults to 180). |
xy_pulse_ppm |
a vector of ppm values for the offset of each sub-simulation. |
resamp |
option to resample the pulse shape. |
fs_resamp |
sampling frequency (Hz) to resample. |
list of resonance amplitudes and frequencies.
PRESS sequence with ideal pulses.
seq_press_ideal(spin_params, ft, ref, TE1 = 0.01, TE2 = 0.02)seq_press_ideal(spin_params, ft, ref, TE1 = 0.01, TE2 = 0.02)
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
TE1 |
TE1 sequence parameter in seconds (TE=TE1+TE2). |
TE2 |
TE2 sequence parameter in seconds. |
list of resonance amplitudes and frequencies.
Simple pulse and acquire sequence with ideal pulses.
seq_pulse_acquire(spin_params, ft, ref, nuc = "1H", acq_delay = 0)seq_pulse_acquire(spin_params, ft, ref, nuc = "1H", acq_delay = 0)
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
nuc |
acquisition nucleus. |
acq_delay |
delay between excitation and acquisition. |
list of resonance amplitudes and frequencies.
sLASER sequence with ideal pulses.
seq_slaser_ideal(spin_params, ft, ref, TE1 = 0.008, TE2 = 0.011, TE3 = 0.009)seq_slaser_ideal(spin_params, ft, ref, TE1 = 0.008, TE2 = 0.011, TE3 = 0.009)
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
TE1 |
first echo time (between exc. and 1st echo) in seconds. |
TE2 |
second echo time (between 2nd echo and 4th echo) in seconds. |
TE3 |
third echo time (between 4th echo and 5th echo) in seconds. |
list of resonance amplitudes and frequencies.
Spin echo sequence with ideal pulses.
seq_spin_echo_ideal(spin_params, ft, ref, nuc = "1H", TE = 0.03)seq_spin_echo_ideal(spin_params, ft, ref, nuc = "1H", TE = 0.03)
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
nuc |
acquisition nucleus. |
TE |
echo time in seconds. |
list of resonance amplitudes and frequencies.
STEAM sequence with ideal pulses.
seq_steam_ideal(spin_params, ft, ref, TE = 0.03, TM = 0.02, amp_scale = 2)seq_steam_ideal(spin_params, ft, ref, TE = 0.03, TM = 0.02, amp_scale = 2)
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
TE |
sequence parameter in seconds. |
TM |
sequence parameter in seconds. |
amp_scale |
amplitude scaling factor. Set to 2 (default) to ensure correct scaling for water reference scaling. Set to 1 to maintain the inherent loss of signal associated with STEAM. |
list of resonance amplitudes and frequencies.
See Landheer et al NMR Biomed 2021 34(5):e4129 and Landheer et al MRM 2019 Apr;81(4):2209-2222 for more details on the coherence order filtering method.
seq_steam_ideal_cof(spin_params, ft, ref, TE = 0.03, TM = 0.02, amp_scale = 2)seq_steam_ideal_cof(spin_params, ft, ref, TE = 0.03, TM = 0.02, amp_scale = 2)
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
TE |
sequence parameter in seconds. |
TM |
sequence parameter in seconds. |
amp_scale |
amplitude scaling factor. Set to 2 (default) to ensure correct scaling for water reference scaling. Set to 1 to maintain the inherent loss of signal associated with STEAM. |
list of resonance amplitudes and frequencies.
STEAM sequence with ideal pulses using the z-rotation gradient simulation method described by Young et al JMR 140, 146-152 (1999).
seq_steam_ideal_young( spin_params, ft, ref, TE = 0.03, TM = 0.02, amp_scale = 2 )seq_steam_ideal_young( spin_params, ft, ref, TE = 0.03, TM = 0.02, amp_scale = 2 )
spin_params |
spin system definition. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
TE |
sequence parameter in seconds. |
TM |
sequence parameter in seconds. |
amp_scale |
amplitude scaling factor. Set to 2 (default) to ensure correct scaling for water reference scaling. Set to 1 to maintain the inherent loss of signal associated with STEAM. |
list of resonance amplitudes and frequencies.
Set the default acquisition parameters.
set_def_acq_paras( ft = getOption("spant.def_ft"), fs = getOption("spant.def_fs"), N = getOption("spant.def_N"), ref = getOption("spant.def_ref"), nuc = getOption("spant.nuc") )set_def_acq_paras( ft = getOption("spant.def_ft"), fs = getOption("spant.def_fs"), N = getOption("spant.def_N"), ref = getOption("spant.def_ref"), nuc = getOption("spant.nuc") )
ft |
transmitter frequency in Hz. |
fs |
sampling frequency in Hz. |
N |
number of data points in the spectral dimension. |
ref |
reference value for ppm scale. |
nuc |
resonant nucleus. |
Set the command to run the LCModel command-line program.
set_lcm_cmd(cmd)set_lcm_cmd(cmd)
cmd |
path to binary. |
Apply line-broadening to an mrs_data object to achieve a specified linewidth.
set_lw(mrs_data, lw, xlim = c(4, 0.5), lg = 1, mask_narrow = TRUE)set_lw(mrs_data, lw, xlim = c(4, 0.5), lg = 1, mask_narrow = TRUE)
mrs_data |
data in. |
lw |
target linewidth in units of ppm. |
xlim |
region to search for peaks to obtain a linewidth estimate. |
lg |
Lorentz-Gauss lineshape parameter. |
mask_narrow |
masks spectra where the requested linewidth is too narrow, if set FALSE the spectra are not changed. |
line-broadened data.
Set the masked voxels in a 2D MRSI dataset to given spectrum.
set_mask_xy_mat(mrs_data, mask, mask_mrs_data)set_mask_xy_mat(mrs_data, mask, mask_mrs_data)
mrs_data |
MRSI data object. |
mask |
matrix of boolean values specifying the voxels to set, where a value of TRUE indicates the voxel should be set to mask_mrs_data. |
mask_mrs_data |
the spectral data to be assigned to the masked voxels. |
updated dataset.
Set the number of transients for an mrs_data object.
set_Ntrans(mrs_data, n_trans)set_Ntrans(mrs_data, n_trans)
mrs_data |
MRS data. |
n_trans |
number of acquired transients. |
Set the precompute mode.
set_precomp_mode(mode = NA)set_precomp_mode(mode = NA)
mode |
can be one of: "default", "overwrite", "clean" or "disabled". |
Set the verbosity of the precompute function.
set_precomp_verbose(verbose = NA)set_precomp_verbose(verbose = NA)
verbose |
can be TRUE or FALSE. |
Set the ppm reference value (eg ppm value at 0Hz).
set_ref(mrs_data, ref)set_ref(mrs_data, ref)
mrs_data |
MRS data. |
ref |
reference value for ppm scale. |
Set the number of time-domain data points, truncating or zero-filling as appropriate.
set_td_pts(mrs_data, pts)set_td_pts(mrs_data, pts)
mrs_data |
MRS data. |
pts |
number of data points. |
MRS data with pts data points.
Set the command to run the TARQUIN command-line program.
set_tqn_cmd(cmd)set_tqn_cmd(cmd)
cmd |
path to binary. |
Set the repetition time of an MRS dataset.
set_tr(mrs_data, tr)set_tr(mrs_data, tr)
mrs_data |
MRS data. |
tr |
repetition time in seconds. |
updated mrs_data set.
Apply a frequency shift to MRS data.
shift(mrs_data, shift, units = "ppm")shift(mrs_data, shift, units = "ppm")
mrs_data |
MRS data. |
shift |
frequency shift (in ppm by default). |
units |
of the shift ("ppm" or "hz"). |
frequency shifted MRS data.
Apply frequency shifts to basis set signals.
shift_basis(basis, shifts)shift_basis(basis, shifts)
basis |
the basis to apply the shift to. |
shifts |
a vector of frequency shifts to apply in ppm units. Must be the same length as there are basis elements, or one value to be applied to all elements. |
modified basis set object.
Simulate a basis set object.
sim_basis( mol_list, pul_seq = seq_pulse_acquire, acq_paras = def_acq_paras(), xlim = NULL, auto_scale = FALSE, use_basis_cache = FALSE, verbose = FALSE, ... )sim_basis( mol_list, pul_seq = seq_pulse_acquire, acq_paras = def_acq_paras(), xlim = NULL, auto_scale = FALSE, use_basis_cache = FALSE, verbose = FALSE, ... )
mol_list |
list of |
pul_seq |
pulse sequence function to use. |
acq_paras |
list of acquisition parameters or an mrs_data object. See
|
xlim |
ppm range limiting signals to be simulated. |
auto_scale |
scale the basis based on the intensity of a singlet resonance. Needed for sequences with spatial simulation. |
use_basis_cache |
create and use a cache of simulated basis sets stored in the "spant_basis_cache" folder in the users home directory. Defaults to FALSE. |
verbose |
output simulation progress and timings. |
... |
extra parameters to pass to the pulse sequence function. |
basis object.
Simulate a basis-set suitable for 1H brain MRS analysis acquired with a PRESS sequence. Note, ideal pulses are assumed.
sim_basis_1h_brain( pul_seq = seq_press_ideal, acq_paras = def_acq_paras(), xlim = c(0.5, 4.2), lcm_compat = FALSE, ... )sim_basis_1h_brain( pul_seq = seq_press_ideal, acq_paras = def_acq_paras(), xlim = c(0.5, 4.2), lcm_compat = FALSE, ... )
pul_seq |
pulse sequence function to use. |
acq_paras |
list of acquisition parameters or an mrs_data object. See
|
xlim |
range of frequencies to simulate in ppm. |
lcm_compat |
exclude lipid and MM signals for use with default LCModel options. |
... |
extra parameters to pass to the pulse sequence function. |
basis object.
Simulate a basis-set suitable for 1H brain MRS analysis acquired with a PRESS sequence. Note, ideal pulses are assumed.
sim_basis_1h_brain_press( acq_paras = def_acq_paras(), xlim = c(0.5, 4.2), lcm_compat = FALSE, TE1 = 0.01, TE2 = 0.02 )sim_basis_1h_brain_press( acq_paras = def_acq_paras(), xlim = c(0.5, 4.2), lcm_compat = FALSE, TE1 = 0.01, TE2 = 0.02 )
acq_paras |
list of acquisition parameters or an mrs_data object. See
|
xlim |
range of frequencies to simulate in ppm. |
lcm_compat |
exclude lipid and MM signals for use with default LCModel options. |
TE1 |
TE1 of PRESS sequence (TE = TE1 + TE2). |
TE2 |
TE2 of PRESS sequence. |
basis object.
Simulate a macromolecular and lipid basis-set suitable for 1H brain MRS analysis.
sim_basis_mm_lip_lcm(acq_paras = def_acq_paras())sim_basis_mm_lip_lcm(acq_paras = def_acq_paras())
acq_paras |
list of acquisition parameters or an mrs_data object. See
|
basis object.
Simulate a basis file using TARQUIN.
sim_basis_tqn( fs = def_fs(), ft = def_ft(), N = def_N(), ref = def_ref(), opts = NULL )sim_basis_tqn( fs = def_fs(), ft = def_ft(), N = def_N(), ref = def_ref(), opts = NULL )
fs |
sampling frequency |
ft |
transmitter frequency |
N |
number of data points |
ref |
chemical shift reference |
opts |
list of options to pass to TARQUIN. |
## Not run: write_basis_tqn('test.basis',mrs_data,c("--echo","0.04")) ## End(Not run)## Not run: write_basis_tqn('test.basis',mrs_data,c("--echo","0.04")) ## End(Not run)
Simulate MRS data with a similar appearance to normal brain (by default).
sim_brain_1h( acq_paras = def_acq_paras(), type = "normal_v2", pul_seq = seq_slaser_ideal, xlim = c(0.5, 4.2), full_output = FALSE, amps = NULL, basis_lb = NULL, zero_lip_mm = FALSE, remove_lip_mm = FALSE, ... )sim_brain_1h( acq_paras = def_acq_paras(), type = "normal_v2", pul_seq = seq_slaser_ideal, xlim = c(0.5, 4.2), full_output = FALSE, amps = NULL, basis_lb = NULL, zero_lip_mm = FALSE, remove_lip_mm = FALSE, ... )
acq_paras |
list of acquisition parameters or an mrs_data object. See
|
type |
type of spectrum, only "normal" is implemented currently. |
pul_seq |
pulse sequence function to use. |
xlim |
range of frequencies to simulate in ppm. |
full_output |
when FALSE (default) only output the simulated MRS data. When TRUE output a list containing the MRS data, basis set object and corresponding amplitudes. |
amps |
a vector of basis amplitudes may be specified to modify the output spectrum. |
basis_lb |
apply additional Gaussian line-broadening to the basis (Hz). |
zero_lip_mm |
zero the amplitudes of any lipid or macromolecular components based on their name starting with "MM" or "Lip". |
remove_lip_mm |
remove any lipid or macromolecular basis components based on their name starting with "MM" or "Lip". |
... |
extra parameters to pass to the pulse sequence function. |
see full_output option.
mol_parameter object.Simulate a mol_parameter object.
sim_mol( mol, pul_seq = seq_pulse_acquire, ft = def_ft(), ref = def_ref(), fs = def_fs(), N = def_N(), xlim = NULL, ... )sim_mol( mol, pul_seq = seq_pulse_acquire, ft = def_ft(), ref = def_ref(), fs = def_fs(), N = def_N(), xlim = NULL, ... )
mol |
|
pul_seq |
pulse sequence function to use. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
fs |
sampling frequency in Hz. |
N |
number of data points in the spectral dimension. |
xlim |
ppm range limiting signals to be simulated. |
... |
extra parameters to pass to the pulse sequence function. |
mrs_data object.
Simulate an mrs_data object containing simulated Gaussian noise.
sim_noise( sd = 0.1, fs = def_fs(), ft = def_ft(), N = def_N(), ref = def_ref(), nuc = def_nuc(), dyns = 1, fd = TRUE )sim_noise( sd = 0.1, fs = def_fs(), ft = def_ft(), N = def_N(), ref = def_ref(), nuc = def_nuc(), dyns = 1, fd = TRUE )
sd |
standard deviation of the noise. |
fs |
sampling frequency in Hz. |
ft |
transmitter frequency in Hz. |
N |
number of data points in the spectral dimension. |
ref |
reference value for ppm scale. |
nuc |
resonant nucleus. |
dyns |
number of dynamic scans to generate. |
fd |
return data in the frequency-domain (TRUE) or time-domain (FALSE) |
mrs_data object.
Simulate a MRS data object containing a set of simulated resonances.
sim_resonances( freq = 0, amp = 1, lw = 0, lg = 0, phase = 0, freq_ppm = TRUE, acq_paras = def_acq_paras(), fp_scale = TRUE, back_extrap_pts = 0, sum_resonances = TRUE )sim_resonances( freq = 0, amp = 1, lw = 0, lg = 0, phase = 0, freq_ppm = TRUE, acq_paras = def_acq_paras(), fp_scale = TRUE, back_extrap_pts = 0, sum_resonances = TRUE )
freq |
resonance frequency. |
amp |
resonance amplitude. |
lw |
line width in Hz. |
lg |
Lorentz-Gauss lineshape parameter (between 0 and 1). |
phase |
phase in degrees. |
freq_ppm |
frequencies are given in ppm units if set to TRUE, otherwise Hz are assumed. |
acq_paras |
list of acquisition parameters. See
|
fp_scale |
multiply the first data point by 0.5. |
back_extrap_pts |
number of data points to back extrapolate. |
sum_resonances |
sum all resonances (default is TRUE), otherwise return a dynamic mrs_data object. |
MRS data object.
sim_data <- sim_resonances(freq = 2, lw = 5)sim_data <- sim_resonances(freq = 2, lw = 5)
Simulate an ideal pulse excitation profile by smoothing a top-hat function with a Gaussian.
sim_th_excit_profile(bw = 1500, sigma = 50, fa = 180)sim_th_excit_profile(bw = 1500, sigma = 50, fa = 180)
bw |
top-hat bandwidth (Hz). |
sigma |
Gaussian width smoothing parameter (Hz). |
fa |
intended flip angle of the pulse. |
data frame containing the frequency scale, excitation profile and corresponding flip-angles.
Simulate an mrs_data object containing complex zero valued samples.
sim_zero( fs = def_fs(), ft = def_ft(), N = def_N(), ref = def_ref(), nuc = def_nuc(), dyns = 1 )sim_zero( fs = def_fs(), ft = def_ft(), N = def_N(), ref = def_ref(), nuc = def_nuc(), dyns = 1 )
fs |
sampling frequency in Hz. |
ft |
transmitter frequency in Hz. |
N |
number of data points in the spectral dimension. |
ref |
reference value for ppm scale. |
nuc |
resonant nucleus. |
dyns |
number of dynamic scans to generate. |
mrs_data object.
Smooth data across the dynamic dimension with a Gaussian kernel.
smooth_dyns(mrs_data, sigma)smooth_dyns(mrs_data, sigma)
mrs_data |
data to be smoothed. |
sigma |
standard deviation of the underlying Gaussian kernel in seconds. |
smoothed mrs_data object.
Sort the basis-set elements alphabetically.
sort_basis(basis)sort_basis(basis)
basis |
input basis. |
sorted basis.
Simulate and fit some spectra with ABfit for benchmarking purposes. Basic timing and performance metrics will be printed.
spant_abfit_benchmark(noise_reps = 10, return_res = FALSE, opts = abfit_opts())spant_abfit_benchmark(noise_reps = 10, return_res = FALSE, opts = abfit_opts())
noise_reps |
number of spectra to fit with differing noise samples. |
return_res |
return a list of fit_result objects. |
opts |
ABfit options structure. |
Example MEGA-PRESS data with significant B0 drift.
spant_mpress_driftspant_mpress_drift
An object of class mrs_data of dimension 1 x 1 x 1 x 1 x 40 x 1 x 1024.
Simulate an example fMRS dataset for a block design fMRS experiment and export a BIDS structure.
spant_sim_fmrs_dataset(output_dir = NULL)spant_sim_fmrs_dataset(output_dir = NULL)
output_dir |
output directory for the BIDS data. Defaults to : "HOME/sim_fmrs_dataset/data". |
Simulate a typical metabolite basis set for benchmarking. Timing metrics will be printed on completion.
spant_simulation_benchmark(sim_reps = 10, N = 1024)spant_simulation_benchmark(sim_reps = 10, N = 1024)
sim_reps |
number of times to simulate the basis set. |
N |
number of FID data points to simulate. |
An implementation of the method published by Goryawala et al MRM 79(6) 2886-2895 (2018). "Spectral decomposition for resolving partial volume effects in MRSI".
spec_decomp(mrs_data, wm, gm, norm_fractions = TRUE)spec_decomp(mrs_data, wm, gm, norm_fractions = TRUE)
mrs_data |
data to be decomposed into white and gray matter spectra. |
wm |
vector of white matter contributions to each voxel. |
gm |
vector of gray matter contributions to each voxel. |
norm_fractions |
option to normalise the wm, gm vectors for each voxel. |
a list of two mrs_data objects corresponding to the two tissue types.
Perform a mathematical operation on a spectral region.
spec_op( mrs_data, xlim = NULL, operator = "sum", freq_scale = "ppm", mode = "re" )spec_op( mrs_data, xlim = NULL, operator = "sum", freq_scale = "ppm", mode = "re" )
mrs_data |
MRS data. |
xlim |
spectral range to be integrated (defaults to full range). |
operator |
can be "sum" (default), "mean", "l2", "max", "max_cplx, "min" or "max-min". |
freq_scale |
units of xlim, can be : "ppm", "hz" or "points". |
mode |
spectral mode, can be : "re", "im", "mod" or "cplx". |
an array of integral values.
Create a spin system object for pulse sequence simulation.
spin_sys(spin_params, ft, ref, precomp_jc_H = NULL, precomp_Iz = NULL)spin_sys(spin_params, ft, ref, precomp_jc_H = NULL, precomp_Iz = NULL)
spin_params |
an object describing the spin system properties. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
precomp_jc_H |
use a precomputed J-coupling H matrix to save time. |
precomp_Iz |
use precomputed Iz matrices to save time. |
spin system object.
Convert SPM style segmentation files to a single categorical image where the numerical values map as: 0) Other, 1) CSF, 2) GM and 3) WM.
spm_pve2categorical(fname)spm_pve2categorical(fname)
fname |
any of the segmentation files (eg c1_MY_T1.nii). |
nifti object.
Signal space projection method as described in: Tsai SY, Lin YR, Lin HY, Lin FH. Reduction of lipid contamination in MR spectroscopy imaging using signal space projection. Magn Reson Med 2019 Mar;81(3):1486-1498.
ssp(mrs_data, comps = 5, xlim = c(1.5, 0.8))ssp(mrs_data, comps = 5, xlim = c(1.5, 0.8))
mrs_data |
MRS data object. |
comps |
the number of spatial components to use. |
xlim |
spectral range (in ppm) covering the lipid signals. |
lipid suppressed mrs_data object.
Produce a plot with multiple traces.
stackplot(x, ...)stackplot(x, ...)
x |
object for plotting. |
... |
arguments to be passed to methods. |
fit_result with
individual basis set components shown.Plot the fitting results of an object of class fit_result with
individual basis set components shown.
## S3 method for class 'fit_result' stackplot( x, xlim = NULL, y_offset = 0, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1, n = NULL, sub_bl = FALSE, labels = FALSE, label_names = NULL, sig_col = "black", restore_def_par = TRUE, omit_signals = NULL, combine_lipmm = FALSE, combine_metab = FALSE, mar = NULL, show_grid = TRUE, grid_nx = NULL, grid_ny = NA, invert_fit = FALSE, ... )## S3 method for class 'fit_result' stackplot( x, xlim = NULL, y_offset = 0, dyn = 1, x_pos = 1, y_pos = 1, z_pos = 1, coil = 1, n = NULL, sub_bl = FALSE, labels = FALSE, label_names = NULL, sig_col = "black", restore_def_par = TRUE, omit_signals = NULL, combine_lipmm = FALSE, combine_metab = FALSE, mar = NULL, show_grid = TRUE, grid_nx = NULL, grid_ny = NA, invert_fit = FALSE, ... )
x |
fit_result object. |
xlim |
the range of values to display on the x-axis, eg xlim = c(4,1). |
y_offset |
separate basis signals in the y-axis direction by this value. |
dyn |
the dynamic index to plot. |
x_pos |
the x index to plot. |
y_pos |
the y index to plot. |
z_pos |
the z index to plot. |
coil |
the coil element number to plot. |
n |
single index element to plot (overrides other indices when given). |
sub_bl |
subtract the baseline from the data and fit (logical). |
labels |
print signal labels at the right side of the plot. |
label_names |
provide a character vector of signal names to replace the defaults determined from the basis set. |
sig_col |
colour of individual signal components. |
restore_def_par |
restore default plotting par values after the plot has been made. |
omit_signals |
a character vector of basis signal names to be removed from the plot. |
combine_lipmm |
combine all basis signals with names starting with "Lip" or "MM". |
combine_metab |
combine all basis signals with names not starting with "Lip" or "MM". |
mar |
option to adjust the plot margins. See ?par. |
show_grid |
plot gridlines behind the data (logical). Defaults to TRUE. |
grid_nx |
number of cells of the grid in x and y direction. When NULL the grid aligns with the tick marks on the corresponding default axis (i.e., tickmarks as computed by axTicks). When NA, no grid lines are drawn in the corresponding direction. |
grid_ny |
as above. |
invert_fit |
show the fit result "upside-down"/ |
... |
further arguments to plot method. |
Stackplot plotting method for objects of class mrs_data.
## S3 method for class 'mrs_data' stackplot( x, xlim = NULL, mode = "re", x_units = NULL, fd = TRUE, col = NULL, alpha = NULL, x_offset = 0, y_offset = 0, plot_dim = NULL, x_pos = NULL, y_pos = NULL, z_pos = NULL, dyn = 1, coil = 1, bty = NULL, labels = NULL, lab_cex = 1, bl_lty = NULL, restore_def_par = TRUE, show_grid = NULL, grid_nx = NULL, grid_ny = NA, lwd = NULL, vline = NULL, vline_lty = 2, vline_col = "red", mar = NULL, ... )## S3 method for class 'mrs_data' stackplot( x, xlim = NULL, mode = "re", x_units = NULL, fd = TRUE, col = NULL, alpha = NULL, x_offset = 0, y_offset = 0, plot_dim = NULL, x_pos = NULL, y_pos = NULL, z_pos = NULL, dyn = 1, coil = 1, bty = NULL, labels = NULL, lab_cex = 1, bl_lty = NULL, restore_def_par = TRUE, show_grid = NULL, grid_nx = NULL, grid_ny = NA, lwd = NULL, vline = NULL, vline_lty = 2, vline_col = "red", mar = NULL, ... )
x |
object of class mrs_data. |
xlim |
the range of values to display on the x-axis, eg xlim = c(4,1). |
mode |
representation of the complex numbers to be plotted, can be one of: "re", "im", "mod" or "arg". |
x_units |
the units to use for the x-axis, can be one of: "ppm", "hz", "points" or "seconds". |
fd |
display data in the frequency-domain (default), or time-domain (logical). |
col |
set the colour of the line, eg col = rgb(1, 0, 0, 0.5). |
alpha |
set the line transparency, eg alpha = 0.5 is 50% transparency. Overrides any transparency levels set by col. |
x_offset |
separate plots in the x-axis direction by this value. Default value is 0. |
y_offset |
separate plots in the y-axis direction by this value. |
plot_dim |
the dimension to display on the y-axis, can be one of: "dyn", "x", "y", "z", "coil" or NULL. If NULL (the default) all spectra will be collapsed into the dynamic dimension and displayed. |
x_pos |
the x index to plot. |
y_pos |
the y index to plot. |
z_pos |
the z index to plot. |
dyn |
the dynamic index to plot. |
coil |
the coil element number to plot. |
bty |
option to draw a box around the plot. See ?par. |
labels |
add labels to each data item. |
lab_cex |
label size. |
bl_lty |
linetype for the y = 0 baseline trace. A default value NULL results in no baseline being plotted. |
restore_def_par |
restore default plotting par values after the plot has been made. |
show_grid |
plot gridlines behind the data (logical). Defaults to TRUE. |
grid_nx |
number of cells of the grid in x and y direction. When NULL the grid aligns with the tick marks on the corresponding default axis (i.e., tickmarks as computed by axTicks). When NA, no grid lines are drawn in the corresponding direction. |
grid_ny |
as above. |
lwd |
plot linewidth. |
vline |
x-value to draw a vertical line. |
vline_lty |
linetype for the vertical line. |
vline_col |
colour for the vertical line. |
mar |
option to adjust the plot margins. See ?par. |
... |
other arguments to pass to the matplot method. |
Subtract the first dynamic spectrum from a dynamic series.
sub_first_dyn(mrs_data, scale = 1)sub_first_dyn(mrs_data, scale = 1)
mrs_data |
dynamic MRS data. |
scale |
scale factor for the first spectrum. |
subtracted data.
Subtract the mean dynamic spectrum from a dynamic series.
sub_mean_dyns(mrs_data, scale = 1)sub_mean_dyns(mrs_data, scale = 1)
mrs_data |
dynamic MRS data. |
scale |
scale factor for the mean spectrum. |
subtracted data.
Subtract the median dynamic spectrum from a dynamic series.
sub_median_dyns(mrs_data, scale = 1)sub_median_dyns(mrs_data, scale = 1)
mrs_data |
dynamic MRS data. |
scale |
scale factor for the medium spectrum. |
subtracted data.
Calculate the sum across receiver coil elements.
sum_coils(mrs_data)sum_coils(mrs_data)
mrs_data |
MRS data split across receiver coil elements. |
sum across coil elements.
Calculate the sum of data dynamics.
sum_dyns(mrs_data)sum_dyns(mrs_data)
mrs_data |
dynamic MRS data. |
sum of data dynamics.
Sum two mrs_data objects.
sum_mrs(a, b, force = FALSE)sum_mrs(a, b, force = FALSE)
a |
first mrs_data object to be summed. |
b |
second mrs_data object to be summed. |
force |
set to TRUE to force mrs_data objects to be summed, even if they are in different time/frequency domains. |
a + b
Return the sum of a list of mrs_data objects.
sum_mrs_list(mrs_list)sum_mrs_list(mrs_list)
mrs_list |
list of mrs_data objects. |
sum mrs_data object.
Output a table of fit amplitudes and error estimates for a single-voxel fit.
sv_res_table(fit_res, format_out = FALSE)sv_res_table(fit_res, format_out = FALSE)
fit_res |
input vector. |
format_out |
reduce the accuracy of values to aid table formatting. |
data.frame of values.
Standard SVS 1H brain analysis pipeline.
svs_1h_brain_analysis( metab, basis = NULL, w_ref = NULL, mri_seg = NULL, mri = NULL, output_dir = NULL, extra = NULL, decimate = NULL, rats_corr = TRUE, ecc = FALSE, comb_dyns = TRUE, hsvd_filt = FALSE, scale_amps = TRUE, te = NULL, tr = NULL, preproc_only = FALSE, method = "ABFIT", opts = NULL )svs_1h_brain_analysis( metab, basis = NULL, w_ref = NULL, mri_seg = NULL, mri = NULL, output_dir = NULL, extra = NULL, decimate = NULL, rats_corr = TRUE, ecc = FALSE, comb_dyns = TRUE, hsvd_filt = FALSE, scale_amps = TRUE, te = NULL, tr = NULL, preproc_only = FALSE, method = "ABFIT", opts = NULL )
metab |
filepath or mrs_data object containing MRS metabolite data. |
basis |
basis set object to use for analysis. |
w_ref |
filepath or mrs_data object containing MRS water reference data. |
mri_seg |
filepath or nifti object containing segmented MRI data. |
mri |
filepath or nifti object containing anatomical MRI data. |
output_dir |
directory path to output fitting results. |
extra |
data.frame with one row containing additional information to be attached to the fit results table. |
decimate |
option to decimate the input data by a factor of two. The default value of NULL does not perform decimation unless the spectral width is greater than 20 PPM. |
rats_corr |
option to perform rats correction, defaults to TRUE. |
ecc |
option to perform water reference based eddy current correction, defaults to FALSE. |
comb_dyns |
option to combine dynamic scans, defaults to TRUE. |
hsvd_filt |
option to apply hsvd water removal, defaults to FALSE. |
scale_amps |
option to scale metabolite amplitude estimates, defaults to TRUE. |
te |
metabolite mrs data echo time in seconds. |
tr |
metabolite mrs data repetition time in seconds. |
preproc_only |
only perform the preprocessing steps and omit fitting. The preprocessed metabolite data will be returned in this case. |
method |
analysis method to use, see fit_mrs help. |
opts |
options to pass to the analysis method. |
a fit_result or mrs_data object depending on the preproc_only option.
Note this function is still under development and liable to changes.
svs_1h_brain_analysis_dev( metab, w_ref = NULL, output_dir = NULL, basis = NULL, p_vols = NULL, append_basis = NULL, remove_basis = NULL, dfp_corr = FALSE, omit_bad_dynamics = FALSE, te = NULL, tr = NULL, output_ratio = "tCr", ecc = FALSE, abfit_opts = NULL, verbose = FALSE )svs_1h_brain_analysis_dev( metab, w_ref = NULL, output_dir = NULL, basis = NULL, p_vols = NULL, append_basis = NULL, remove_basis = NULL, dfp_corr = FALSE, omit_bad_dynamics = FALSE, te = NULL, tr = NULL, output_ratio = "tCr", ecc = FALSE, abfit_opts = NULL, verbose = FALSE )
metab |
filepath or mrs_data object containing MRS metabolite data. |
w_ref |
filepath or mrs_data object containing MRS water reference data. |
output_dir |
directory path to output fitting results. |
basis |
precompiled basis set object to use for analysis. |
p_vols |
a numeric vector of partial volumes expressed as percentages. Defaults to 100% white matter. A voxel containing 100% gray matter tissue would use : p_vols = c(WM = 0, GM = 100, CSF = 0). |
append_basis |
names of extra signals to add to the default basis. Eg append_basis = c("peth", "cit"). Cannot be used with precompiled basis sets. |
remove_basis |
names of signals to remove from the basis. Cannot be used with precompiled basis sets. |
dfp_corr |
perform dynamic frequency and phase correction using the RATS method. |
omit_bad_dynamics |
detect and remove bad dynamics. |
te |
metabolite mrs data echo time in seconds. If not supplied this will be guessed from the metab data file. |
tr |
metabolite mrs data repetition time in seconds. If not supplied this will be guessed from the metab data file. |
output_ratio |
optional string to specify a metabolite ratio to output. Defaults to "tCr" and multiple metabolites may be specified for multiple outputs. Set as NULL to omit. |
ecc |
option to perform water reference based eddy current correction, defaults to FALSE. |
abfit_opts |
options to pass to ABfit. |
verbose |
output potentially useful information. |
metab <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") w_ref <- system.file("extdata", "philips_spar_sdat_W.SDAT", package = "spant") ## Not run: fit_result <- svs_1h_brain_analysis(metab, w_ref, "fit_res_dir") ## End(Not run)metab <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package = "spant") w_ref <- system.file("extdata", "philips_spar_sdat_W.SDAT", package = "spant") ## Not run: fit_result <- svs_1h_brain_analysis(metab, w_ref, "fit_res_dir") ## End(Not run)
Batch interface to the standard SVS 1H brain analysis pipeline.
svs_1h_brain_batch_analysis( metab_list, w_ref_list = NULL, mri_seg_list = NULL, mri_list = NULL, output_dir_list = NULL, extra = NULL, ... )svs_1h_brain_batch_analysis( metab_list, w_ref_list = NULL, mri_seg_list = NULL, mri_list = NULL, output_dir_list = NULL, extra = NULL, ... )
metab_list |
list of file paths or mrs_data objects containing MRS metabolite data. |
w_ref_list |
list of file paths or mrs_data objects containing MRS water reference data. |
mri_seg_list |
list of file paths or nifti objects containing segmented MRI data. |
mri_list |
list of file paths or nifti objects containing anatomical MRI data. |
output_dir_list |
list of directory paths to output fitting results. |
extra |
a data frame with the same number of rows as metab_list, containing additional information to be attached to the fit results table. |
... |
additional options to be passed to the svs_1h_brain_analysis function. |
a list of fit_result objects.
Perform a t-test on spectral data points.
t_test_spec(mrs_data, group)t_test_spec(mrs_data, group)
mrs_data |
an mrs_data object with spectra in the dynamic dimension. |
group |
vector describing the group membership of each dynamic spectrum. |
a list of statistical results.
Time-domain convolution based filter described by: Marion D, Ikura M, Bax A. Improved solvent suppression in one-dimensional and twodimensional NMR spectra by convolution of time-domain data. J Magn Reson 1989;84:425-430.
td_conv_filt(mrs_data, K = 25, ext = 1)td_conv_filt(mrs_data, K = 25, ext = 1)
mrs_data |
MRS data to be filtered. |
K |
window width in data points. |
ext |
point separation for linear extrapolation. |
Transform time-domain data to the frequency-domain.
td2fd(mrs_data)td2fd(mrs_data)
mrs_data |
MRS data in time-domain representation. |
MRS data in frequency-domain representation.
An implementation of the method published by Near et al MRM 73:44-50 (2015).
tdsr(mrs_data, ref = NULL, xlim = c(4, 0.5), max_t = 0.2)tdsr(mrs_data, ref = NULL, xlim = c(4, 0.5), max_t = 0.2)
mrs_data |
MRS data to be corrected. |
ref |
optional MRS data to use as a reference, the mean of all dynamics is used if this argument is not supplied. |
xlim |
optional frequency range to perform optimisation, set to NULL to use the full range. |
max_t |
truncate the FID when longer than max_t to reduce time taken. |
a list containing the corrected data; phase and shift values in units of degrees and Hz respectively.
Return the echo time of an MRS dataset.
te(mrs_data)te(mrs_data)
mrs_data |
MRS data. |
echo time in seconds.
Return the repetition time of an MRS dataset.
tr(mrs_data)tr(mrs_data)
mrs_data |
MRS data. |
repetition time in seconds.
Return a list of options for VARPRO based fitting with 3 free parameters.
varpro_3_para_opts( nstart = 20, init_damping = 2, maxiters = 200, max_shift = 5, max_damping = 5, anal_jac = FALSE, bl_smth_pts = 80 )varpro_3_para_opts( nstart = 20, init_damping = 2, maxiters = 200, max_shift = 5, max_damping = 5, anal_jac = FALSE, bl_smth_pts = 80 )
nstart |
position in the time-domain to start fitting, units of data points. |
init_damping |
starting value for the global Gaussian line-broadening term - measured in Hz. |
maxiters |
maximum number of levmar iterations to perform. |
max_shift |
maximum global shift allowed, measured in Hz. |
max_damping |
maximum damping allowed, FWHM measured in Hz. |
anal_jac |
option to use the analytic or numerical Jacobian (logical). |
bl_smth_pts |
number of data points to use in the baseline smoothing calculation. |
list of options.
Return a list of options for a basic VARPRO analysis.
varpro_basic_opts(method = "fd_re", nnls = TRUE, ppm_left = 4, ppm_right = 0.2)varpro_basic_opts(method = "fd_re", nnls = TRUE, ppm_left = 4, ppm_right = 0.2)
method |
one of "td", "fd", "fd_re". |
nnls |
restrict basis amplitudes to non-negative values. |
ppm_left |
downfield frequency limit for the fitting range (ppm). |
ppm_right |
upfield frequency limit for the fitting range (ppm). |
full list of options.
Return a list of options for VARPRO based fitting.
varpro_opts( nstart = 20, init_g_damping = 2, maxiters = 200, max_shift = 5, max_g_damping = 5, max_ind_damping = 5, anal_jac = TRUE, bl_smth_pts = 80 )varpro_opts( nstart = 20, init_g_damping = 2, maxiters = 200, max_shift = 5, max_g_damping = 5, max_ind_damping = 5, anal_jac = TRUE, bl_smth_pts = 80 )
nstart |
position in the time-domain to start fitting, units of data points. |
init_g_damping |
starting value for the global Gaussian line-broadening term - measured in Hz. |
maxiters |
maximum number of levmar iterations to perform. |
max_shift |
maximum shift allowed to each element in the basis set, measured in Hz. |
max_g_damping |
maximum permitted global Gaussian line-broadening. |
max_ind_damping |
maximum permitted Lorentzian line-broadening for each element in the basis set, measured in Hz. |
anal_jac |
option to use the analytic or numerical Jacobian (logical). |
bl_smth_pts |
number of data points to use in the baseline smoothing calculation. |
list of options.
varpro_opts(nstart = 10)varpro_opts(nstart = 10)
Convert a vector into a mrs_data object.
vec2mrs_data( vec, mrs_data = NULL, fs = NULL, ft = NULL, ref = NULL, nuc = NULL, dyns = 1, fd = FALSE )vec2mrs_data( vec, mrs_data = NULL, fs = NULL, ft = NULL, ref = NULL, nuc = NULL, dyns = 1, fd = FALSE )
vec |
the data vector. |
mrs_data |
example data to copy acquisition parameters from. |
fs |
sampling frequency in Hz. |
ft |
transmitter frequency in Hz. |
ref |
reference value for ppm scale. |
nuc |
resonant nucleus. |
dyns |
replicate the data across the dynamic dimension. |
fd |
flag to indicate if the vector is in the frequency domain (logical). |
mrs_data object.
Write a basis object to an LCModel .basis formatted file.
write_basis(basis, basis_file, fwhmba = 0.1)write_basis(basis, basis_file, fwhmba = 0.1)
basis |
basis object to be exported. |
basis_file |
path to basis file to be generated. |
fwhmba |
parameter used by LCModel. |
Generate a basis file using TARQUIN.
write_basis_tqn(basis_file, metab_data, opts = NULL)write_basis_tqn(basis_file, metab_data, opts = NULL)
basis_file |
filename of the basis file to be generated. |
metab_data |
MRS data object to match the generated basis parameters. |
opts |
list of options to pass to TARQUIN. |
## Not run: write_basis_tqn('test.basis',mrs_data,c("--echo","0.04")) ## End(Not run)## Not run: write_basis_tqn('test.basis',mrs_data,c("--echo","0.04")) ## End(Not run)
Write MRS data object to file.
write_mrs(mrs_data, fname, format = NULL, force = FALSE)write_mrs(mrs_data, fname, format = NULL, force = FALSE)
mrs_data |
object to be written to file, or list of mrs_data objects. |
fname |
one or more filenames to output. |
format |
string describing the data format. Must be one of the following : "nifti", "dpt", "lcm_raw", "rds". If not specified, the format will be guessed from the filename extension. |
force |
set to TRUE to overwrite any existing files. |
Write MRS data object to file in NIFTI format.
write_mrs_nifti(mrs_data, fname)write_mrs_nifti(mrs_data, fname)
mrs_data |
object to be written to file. |
fname |
the filename of the output NIFTI MRS data. |
Write an ASCII formatted pulse file.
write_pulse_ascii(pulse, path)write_pulse_ascii(pulse, path)
pulse |
pulse data object. |
path |
file path for export. |
Fade a spectrum to zero by frequency domain multiplication with a tanh function. Note this operation distorts data points at the end of the FID.
zero_fade_spec(mrs_data, start_ppm, end_ppm)zero_fade_spec(mrs_data, start_ppm, end_ppm)
mrs_data |
data to be faded. |
start_ppm |
start point of the fade in ppm units. |
end_ppm |
end point of the fade in ppm units. |
modified mrs_data object.
Zero all coherences including and above a given order.
zero_higher_orders(sys, rho, order)zero_higher_orders(sys, rho, order)
sys |
spin system object. |
rho |
density matrix. |
order |
states higher than or equal to this argument will be set to zero. |
density matrix.
mrs_data object data points at the end of the FID to zero.Set mrs_data object data points at the end of the FID to zero.
zero_td_pts_end(mrs_data, pts)zero_td_pts_end(mrs_data, pts)
mrs_data |
MRS data. |
pts |
number of end points to set to zero. |
modified mrs_data object.
Zero-fill MRS data in the time domain.
zf(x, factor = 2, offset = 0) ## S3 method for class 'list' zf(x, factor = 2, offset = 0) ## S3 method for class 'mrs_data' zf(x, factor = 2, offset = 0) ## S3 method for class 'basis_set' zf(x, factor = 2, offset = 0)zf(x, factor = 2, offset = 0) ## S3 method for class 'list' zf(x, factor = 2, offset = 0) ## S3 method for class 'mrs_data' zf(x, factor = 2, offset = 0) ## S3 method for class 'basis_set' zf(x, factor = 2, offset = 0)
x |
input mrs_data or basis_set object. |
factor |
zero-filling factor, factor of 2 returns a dataset with twice the original data points. |
offset |
number of points from the end of the FID to insert the zero values. |
zero-filled data.
Zero-fill MRSI data in the k-space x-y direction.
zf_xy(mrs_data, factor = 2)zf_xy(mrs_data, factor = 2)
mrs_data |
MRSI data. |
factor |
zero-filling factor, a factor of 2 returns a dataset with twice the original points in the x-y directions. Factors smaller than one are permitted, such that a factor of 0.5 returns half the k-space points in the x-y directions. |
zero-filled data.