def _build_arg_parser():
p = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('in_dwi',
help='Path of the input diffusion volume.')
p.add_argument('in_bval',
help='Path of the bvals file, in FSL format.')
p.add_argument('in_bvec',
help='Path of the bvecs file, in FSL format.')
p.add_argument('frf_file',
help='Path of the FRF file')
p.add_argument('out_fODF',
help='Output path for the fiber ODF coefficients.')
p.add_argument(
'--sh_order', metavar='int', default=8, type=int,
help='SH order used for the CSD. (Default: 8)')
p.add_argument(
'--mask', metavar='',
help='Path to a binary mask. Only the data inside the mask will be '
'used for computations and reconstruction.')
add_force_b0_arg(p)
add_sh_basis_args(p)
add_processes_arg(p)
add_overwrite_arg(p)
return p
def _build_arg_parser():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('in_dwi', help='DWI Nifti image.')
p.add_argument('in_bval', help='b-values filename, in FSL format (.bval).')
p.add_argument('in_bvec', help='b-values filename, in FSL format (.bvec).')
p.add_argument('out_b0', help='Output b0 file(s).')
p.add_argument('--b0_thr',
type=float,
default=0.0,
help='All b-values with values less than or equal '
'to b0_thr are considered as b0s i.e. without '
'diffusion weighting.')
group = p.add_mutually_exclusive_group()
group.add_argument('--all',
action='store_true',
help='Extract all b0. Index number will be appended to '
'the output file.')
group.add_argument('--mean', action='store_true', help='Extract mean b0.')
add_force_b0_arg(p)
add_verbose_arg(p)
return p
def _build_arg_parser():
p = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('in_dwi',
help='Path of the dwi volume.')
p.add_argument('in_bval',
help='Path of the b-value file, in FSL format.')
p.add_argument('in_bvec',
help='Path of the b-vector file, in FSL format.')
p.add_argument('out_sh',
help='Name of the output SH file to save.')
p.add_argument('--sh_order', type=int, default=4,
help='SH order to fit (int). [%(default)s]')
add_sh_basis_args(p)
p.add_argument('--smooth', type=float, default=0.006,
help='Lambda-regularization coefficient in the SH fit '
'(float). [%(default)s]')
p.add_argument('--use_attenuation', action='store_true',
help='If set, will use signal attenuation before fitting '
'the SH (i.e. divide by the b0).')
add_force_b0_arg(p)
p.add_argument('--mask',
help='Path to a binary mask.\nOnly data inside the mask '
'will be used for computations and reconstruction ')
add_overwrite_arg(p)
return p
def _build_arg_parser():
p = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument('in_dwi', help='Path of the input diffusion volume.')
p.add_argument('in_bval', help='Path of the bval file, in FSL format.')
p.add_argument('in_bvec', help='Path of the bvec file, in FSL format.')
p.add_argument('in_wm_frf', help='Text file of WM response function.')
p.add_argument('in_gm_frf', help='Text file of GM response function.')
p.add_argument('in_csf_frf', help='Text file of CSF response function.')
p.add_argument('--sh_order',
metavar='int',
default=8,
type=int,
help='SH order used for the CSD. (Default: 8)')
p.add_argument('--mask',
metavar='',
help='Path to a binary mask. Only the data inside the '
'mask will be used for computations and reconstruction.')
add_force_b0_arg(p)
add_sh_basis_args(p)
add_processes_arg(p)
add_overwrite_arg(p)
p.add_argument('--not_all',
action='store_true',
help='If set, only saves the files specified using the '
'file flags. (Default: False)')
g = p.add_argument_group(title='File flags')
g.add_argument('--wm_out_fODF',
metavar='file',
default='',
help='Output filename for the WM fODF coefficients.')
g.add_argument('--gm_out_fODF',
metavar='file',
default='',
help='Output filename for the GM fODF coefficients.')
g.add_argument('--csf_out_fODF',
metavar='file',
default='',
help='Output filename for the CSF fODF coefficients.')
g.add_argument('--vf',
metavar='file',
default='',
help='Output filename for the volume fractions map.')
g.add_argument('--vf_rgb',
metavar='file',
default='',
help='Output filename for the volume fractions map in rgb.')
return p
def _build_arg_parser():
p = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="References: [1] Tournier et al. NeuroImage 2007")
p.add_argument('input',
help='Path of the input diffusion volume.')
p.add_argument('bvals',
help='Path of the bvals file, in FSL format.')
p.add_argument('bvecs',
help='Path of the bvecs file, in FSL format.')
p.add_argument('frf_file',
help='Path to the output FRF file, in .txt format, '
'saved by Numpy.')
add_force_b0_arg(p)
p.add_argument(
'--mask',
help='Path to a binary mask. Only the data inside the mask will be '
'used for computations and reconstruction. Useful if no white '
'matter mask is available.')
p.add_argument(
'--mask_wm', metavar='',
help='Path to a binary white matter mask. Only the data inside this '
'mask and above the threshold defined by --fa will be used to '
'estimate the fiber response function.')
p.add_argument(
'--fa', dest='fa_thresh', default=0.7, type=float,
help='If supplied, use this threshold as the initial threshold '
'to select single fiber voxels. [%(default)s]')
p.add_argument(
'--min_fa', dest='min_fa_thresh', default=0.5, type=float,
help='If supplied, this is the minimal value that will be tried '
'when looking for single fiber voxels. [%(default)s]')
p.add_argument(
'--min_nvox', default=300, type=int,
help='Minimal number of voxels needing to be identified as single '
'fiber voxels in the automatic estimation. [%(default)s]')
p.add_argument(
'--roi_radius', default=10, type=int,
help='If supplied, use this radius to select single fibers from the '
'tensor to estimate the FRF. The roi will be a cube spanning '
'from the middle of the volume in each direction. [%(default)s]')
p.add_argument(
'--roi_center', metavar='tuple(3)',
help='If supplied, use this center to span the roi of size '
'roi_radius. [center of the 3D volume]')
add_overwrite_arg(p)
p.add_argument('--verbose', '-v', action='store_true',
help='Produce verbose output.')
return p
def _build_arg_parser():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('in_dwi', help='Path of the input diffusion volume.')
p.add_argument('in_bval', help='Path of the bvals file, in FSL format.')
p.add_argument('in_bvec', help='Path of the bvecs file, in FSL format.')
add_overwrite_arg(p)
p.add_argument('--sh_order',
default=4,
type=int,
help='Spherical harmonics order. Must be a positive even '
'number [%(default)s].')
p.add_argument('--mask',
help='Path to a binary mask. Only data inside the mask will'
' be used for computations and reconstruction '
'[%(default)s].')
p.add_argument('--use_qball',
action='store_true',
help='If set, qball will be used as the odf reconstruction'
' model instead of CSA.')
p.add_argument('--not_all',
action='store_true',
help='If set, will only save the files specified using the '
'following flags.')
g = p.add_argument_group(title='File flags')
g.add_argument('--gfa',
default='',
help='Output filename for the generalized fractional '
'anisotropy [gfa.nii.gz].')
g.add_argument('--peaks',
default='',
help='Output filename for the extracted peaks '
'[peaks.nii.gz].')
g.add_argument('--peak_indices',
default='',
help='Output filename for the generated peaks '
'indices on the sphere [peaks_indices.nii.gz].')
g.add_argument('--sh',
default='',
help='Output filename for the spherical harmonics '
'coefficients [sh.nii.gz].')
g.add_argument('--nufo',
default='',
help='Output filename for the NUFO map [nufo.nii.gz].')
g.add_argument('--a_power',
default='',
help='Output filename for the anisotropic power map'
'[anisotropic_power.nii.gz].')
add_force_b0_arg(p)
add_sh_basis_args(p)
add_processes_arg(p)
return p
def _build_arg_parser():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('input', help='Path of the input diffusion volume.')
p.add_argument('bvals', help='Path of the bvals file, in FSL format.')
p.add_argument('bvecs', help='Path of the bvecs file, in FSL format.')
p.add_argument('frf_file', help='Path of the FRF file')
p.add_argument('--sh_order',
metavar='int',
default=8,
type=int,
help='SH order used for the CSD. (Default: 8)')
p.add_argument(
'--mask',
metavar='',
help='Path to a binary mask. Only the data inside the mask will be '
'used for computations and reconstruction.')
p.add_argument(
'--processes',
dest='nbr_processes',
metavar='NBR',
type=int,
help='Number of sub processes to start. Default : cpu count')
p.add_argument(
'--not_all',
action='store_true',
help='If set, only saves the files specified using the file flags. '
'(Default: False)')
add_force_b0_arg(p)
add_sh_basis_args(p)
g = p.add_argument_group(title='File flags')
g.add_argument('--fodf',
metavar='file',
default='',
help='Output filename for the fiber ODF coefficients.')
g.add_argument('--peaks',
metavar='file',
default='',
help='Output filename for the extracted peaks.')
g.add_argument(
'--peak_indices',
metavar='file',
default='',
help='Output filename for the generated peaks indices on the sphere.')
add_overwrite_arg(p)
return p
def _build_arg_parser():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('in_sh', help='Path of the SH volume.')
p.add_argument('out_sf',
help='Name of the output SF file to save (bvals/bvecs will '
'be automatically named when necessary).')
# Sphere vs bvecs choice for SF
directions = p.add_mutually_exclusive_group(required=True)
directions.add_argument('--sphere',
choices=sorted(SPHERE_FILES.keys()),
help='Sphere used for the SH to SF projection. ')
directions.add_argument(
'--in_bvec', help="Directions used for the SH to SF projection.")
p.add_argument('--dtype',
default="float32",
choices=["float32", "float64"],
help="Datatype to use for SF computation and output array."
"'[%(default)s]'")
# Optional args for a DWI-like volume
p.add_argument('--in_bval',
help='b-value file, in FSL format, '
'used to assign a b-value to the '
'output SF and generate a `.bval` file.')
p.add_argument('--in_b0',
help='b0 volume to concatenate to the '
'final SF volume.')
p.add_argument('--out_bval', help="Optional output bval file.")
p.add_argument('--out_bvec', help="Optional output bvec file.")
p.add_argument('--b0_scaling',
action="store_true",
help="Scale resulting SF by the b0 image.")
add_sh_basis_args(p)
p.add_argument('--full_basis',
action="store_true",
help="If true, use a full basis for the input SH "
"coefficients.")
add_processes_arg(p)
add_overwrite_arg(p)
add_force_b0_arg(p)
return p
def _build_arg_parser():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('in_dwi', help='DWI Nifti image.')
p.add_argument('in_bval', help='b-values filename, in FSL format (.bval).')
p.add_argument('in_bvec', help='b-values filename, in FSL format (.bvec).')
p.add_argument('out_b0', help='Output b0 file(s).')
p.add_argument('--b0_thr',
type=float,
default=0.0,
help='All b-values with values less than or equal '
'to b0_thr are considered as b0s i.e. without '
'diffusion weighting. [%(default)s]')
group = p.add_mutually_exclusive_group()
group.add_argument('--all',
action='store_true',
help='Extract all b0. Index number will be appended to '
'the output file.')
group.add_argument('--mean', action='store_true', help='Extract mean b0.')
group.add_argument('--cluster-mean',
action='store_true',
help='Extract mean of each continuous cluster of b0s.')
group.add_argument('--cluster-first',
action='store_true',
help='Extract first b0 of each '
'continuous cluster of b0s.')
p.add_argument('--block-size',
'-s',
metavar='INT',
type=int,
help='Load the data using this block size. '
'Useful\nwhen the data is too large to be '
'loaded in memory.')
p.add_argument('--single-image',
action='store_true',
help='If output b0 volume has multiple time points, only '
'outputs a single image instead of a numbered series '
'of images.')
add_force_b0_arg(p)
add_verbose_arg(p)
return p
def _build_args_parser():
p = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('input',
help='Path of the input diffusion volume.')
p.add_argument('bvals',
help='Path of the bvals file, in FSL format.')
p.add_argument('bvecs',
help='Path of the bvecs file, in FSL format.')
add_overwrite_arg(p)
p.add_argument(
'--mask',
help='Path to a binary mask.\nOnly data inside the mask will be used '
'for computations and reconstruction. (Default: %(default)s)')
p.add_argument(
'--method', dest='method', metavar='method_name', default='WLS',
choices=['WLS', 'LS', 'NLLS', 'restore'],
help='Tensor fit method.\nWLS for weighted least squares' +
'\nLS for ordinary least squares' +
'\nNLLS for non-linear least-squares' +
'\nrestore for RESTORE robust tensor fitting. (Default: %(default)s)')
p.add_argument(
'--not_all', action='store_true', dest='not_all',
help='If set, will only save the metrics explicitly specified using '
'the other metrics flags. (Default: not set).')
g = p.add_argument_group(title='Metrics files flags')
g.add_argument('--ad', dest='ad', metavar='file', default='',
help='Output filename for the axial diffusivity.')
g.add_argument(
'--evecs', dest='evecs', metavar='file', default='',
help='Output filename for the eigenvectors of the tensor.')
g.add_argument(
'--evals', dest='evals', metavar='file', default='',
help='Output filename for the eigenvalues of the tensor.')
g.add_argument(
'--fa', dest='fa', metavar='file', default='',
help='Output filename for the fractional anisotropy.')
g.add_argument(
'--ga', dest='ga', metavar='file', default='',
help='Output filename for the geodesic anisotropy.')
g.add_argument(
'--md', dest='md', metavar='file', default='',
help='Output filename for the mean diffusivity.')
g.add_argument(
'--mode', dest='mode', metavar='file', default='',
help='Output filename for the mode.')
g.add_argument(
'--norm', dest='norm', metavar='file', default='',
help='Output filename for the tensor norm.')
g.add_argument(
'--rgb', dest='rgb', metavar='file', default='',
help='Output filename for the colored fractional anisotropy.')
g.add_argument(
'--rd', dest='rd', metavar='file', default='',
help='Output filename for the radial diffusivity.')
g.add_argument(
'--tensor', dest='tensor', metavar='file', default='',
help='Output filename for the tensor coefficients.')
g = p.add_argument_group(title='Quality control files flags')
g.add_argument(
'--non-physical', dest='p_i_signal', metavar='file', default='',
help='Output filename for the voxels with physically implausible '
'signals \nwhere the mean of b=0 images is below one or more '
'diffusion-weighted images.')
g.add_argument(
'--pulsation', dest='pulsation', metavar='string', default='',
help='Standard deviation map across all diffusion-weighted images '
'and across b=0 images if more than one is available.\nShows '
'pulsation and misalignment artifacts.')
g.add_argument(
'--residual', dest='residual', metavar='file', default='',
help='Output filename for the map of the residual of the tensor fit.')
add_force_b0_arg(p)
return p
def _build_args_parser():
p = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
p.add_argument('input',
help='Path of the input multi-shell DWI dataset.')
p.add_argument('bvals',
help='Path of the bvals file, in FSL format.')
p.add_argument('bvecs',
help='Path of the bvecs file, in FSL format.')
p.add_argument('--mask',
help='Path to a binary mask.' +
'\nOnly data inside the mask will be used '
'for computations and reconstruction. ' +
'\n[Default: None]')
p.add_argument('--tolerance', '-t',
metavar='INT', type=int, default=20,
help='The tolerated distance between the b-values to '
'extract\nand the actual b-values [Default: %(default)s].')
p.add_argument('--min_k', type=float, default=0.0,
help='Minimum kurtosis value in the output maps ' +
'\n(ak, mk, rk). In theory, -3/7 is the min kurtosis ' +
'\nlimit for regions that consist of water confined ' +
'\nto spherical pores (see DIPY example and ' +
'\ndocumentation) [Default: %(default)s].')
p.add_argument('--max_k', type=float, default=3.0,
help='Maximum kurtosis value in the output maps ' +
'\n(ak, mk, rk). In theory, 10 is the max kurtosis' +
'\nlimit for regions that consist of water confined' +
'\nto spherical pores (see DIPY example and ' +
'\ndocumentation) [Default: %(default)s].')
p.add_argument('--smooth', type=float, default=2.5,
help='Smooth input DWI with a 3D Gaussian filter with ' +
'\nfull-width-half-max (fwhm). Kurtosis fitting is ' +
'\nsensitive and outliers occur easily. According to' +
'\ntests on HCP, CB_Brain, Penthera3T, this smoothing' +
'\nis thus turned ON by default with fwhm=2.5. ' +
'\n[Default: %(default)s].')
p.add_argument('--not_all', action='store_true',
help='If set, will only save the metrics explicitly ' +
'\nspecified using the other metrics flags. ' +
'\n[Default: not set].')
g = p.add_argument_group(title='Metrics files flags')
g.add_argument('--ak', metavar='file', default='',
help='Output filename for the axial kurtosis.')
g.add_argument('--mk', metavar='file', default='',
help='Output filename for the mean kurtosis.')
g.add_argument('--rk', metavar='file', default='',
help='Output filename for the radial kurtosis.')
g.add_argument('--msk', metavar='file', default='',
help='Output filename for the mean signal kurtosis.')
g.add_argument('--dki_fa', metavar='file', default='',
help='Output filename for the fractional anisotropy ' +
'from DKI.')
g.add_argument('--dki_md', metavar='file', default='',
help='Output filename for the mean diffusivity from DKI.')
g.add_argument('--dki_ad', metavar='file', default='',
help='Output filename for the axial diffusivity from DKI.')
g.add_argument('--dki_rd', metavar='file', default='',
help='Output filename for the radial diffusivity from DKI.')
g = p.add_argument_group(title='Quality control files flags')
g.add_argument('--dki_residual', metavar='file', default='',
help='Output filename for the map of the residual ' +
'of the tensor fit.')
g.add_argument('--msd', metavar='file', default='',
help='Output filename for the mean signal diffusion ' +
'(powder-average).')
add_force_b0_arg(p)
add_overwrite_arg(p)
return p
def _build_arg_parser():
p = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="References: [1] Tournier et al. NeuroImage 2007")
p.add_argument('in_dwi', help='Path of the input diffusion volume.')
p.add_argument('in_bval', help='Path of the bvals file, in FSL format.')
p.add_argument('in_bvec', help='Path of the bvecs file, in FSL format.')
p.add_argument('frf_file',
help='Path to the output FRF file, in .txt format, '
'saved by Numpy.')
add_force_b0_arg(p)
p.add_argument('--mask',
help='Path to a binary mask. Only the data inside the '
'mask will be used for computations and '
'reconstruction. Useful if no white matter mask '
'is available.')
p.add_argument('--mask_wm',
help='Path to a binary white matter mask. Only the data '
'inside this mask and above the threshold defined '
'by --fa will be used to estimate the fiber response '
'function.')
p.add_argument('--fa',
dest='fa_thresh',
default=0.7,
type=float,
help='If supplied, use this threshold as the initial '
'threshold to select single fiber voxels. '
'[%(default)s]')
p.add_argument('--min_fa',
dest='min_fa_thresh',
default=0.5,
type=float,
help='If supplied, this is the minimal value that will be '
'tried when looking for single fiber '
'voxels. [%(default)s]')
p.add_argument('--min_nvox',
default=300,
type=int,
help='Minimal number of voxels needing to be identified '
'as single fiber voxels in the automatic '
'estimation. [%(default)s]')
p.add_argument('--roi_radii',
nargs='+',
default=[20],
type=int,
help='If supplied, use those radii to select a cuboid roi '
'to estimate the response functions. The roi will be '
'a cuboid spanning from the middle of the volume in '
'each direction with the different radii. The type is '
'either an int or an array-like (3,). [%(default)s]')
p.add_argument('--roi_center',
metavar='tuple(3)',
nargs=3,
type=int,
help='If supplied, use this center to span the roi of size '
'roi_radius. [center of the 3D volume]')
add_overwrite_arg(p)
add_verbose_arg(p)
return p
def buildArgsParser():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument('in_dwi',
help='Path to the input diffusion volume.')
p.add_argument('in_bval',
help='Path to the bval file, in FSL format.')
p.add_argument('in_bvec',
help='Path to the bvec file, in FSL format.')
p.add_argument('out_wm_frf',
help='Path to the output WM frf file, in .txt format.')
p.add_argument('out_gm_frf',
help='Path to the output GM frf file, in .txt format.')
p.add_argument('out_csf_frf',
help='Path to the output CSF frf file, in .txt format.')
p.add_argument(
'--mask',
help='Path to a binary mask. Only the data inside the mask will be '
'used for\ncomputations and reconstruction. Useful if no tissue '
'masks are available.')
p.add_argument(
'--mask_wm',
help='Path to the input WM mask file, used to improve the final WM '
'frf mask.')
p.add_argument(
'--mask_gm',
help='Path to the input GM mask file, used to improve the final GM '
'frf mask.')
p.add_argument(
'--mask_csf',
help='Path to the input CSF mask file, used to improve the final CSF '
'frf mask.')
p.add_argument(
'--fa_thr_wm', default=0.7, type=float,
help='If supplied, use this threshold to select single WM fiber '
'voxels from the FA inside the WM mask defined by mask_wm. Each '
'voxel above this threshold will be selected. [%(default)s]')
p.add_argument(
'--fa_thr_gm', default=0.2, type=float,
help='If supplied, use this threshold to select GM voxels from the FA '
'inside the GM mask defined by mask_gm. Each voxel below this '
'threshold will be selected. [%(default)s]')
p.add_argument(
'--fa_thr_csf', default=0.1, type=float,
help='If supplied, use this threshold to select CSF voxels from the '
'FA inside the CSF mask defined by mask_csf. Each voxel below '
'this threshold will be selected. [%(default)s]')
p.add_argument(
'--md_thr_gm', default=0.0007, type=float,
help='If supplied, use this threshold to select GM voxels from the MD '
'inside the GM mask defined by mask_gm. Each voxel below this '
'threshold will be selected. [%(default)s]')
p.add_argument(
'--md_thr_csf', default=0.003, type=float,
help='If supplied, use this threshold to select CSF voxels from the '
'MD inside the CSF mask defined by mask_csf. Each voxel below '
'this threshold will be selected. [%(default)s]')
p.add_argument(
'--min_nvox', default=100, type=int,
help='Minimal number of voxels needed for each tissue masks '
'in order to proceed to frf estimation. [%(default)s]')
p.add_argument(
'--tolerance', type=int, default=20,
help='The tolerated gap between the b-values to '
'extract and the current b-value. [%(default)s]')
p.add_argument(
'--dti_bval_limit', type=int, default=1200,
help='The highest b-value taken for the DTI model. [%(default)s]')
p.add_argument(
'--roi_radii', default=[10], nargs='+', type=int,
help='If supplied, use those radii to select a cuboid roi '
'to estimate the response functions. The roi will be '
'a cuboid spanning from the middle of the volume in '
'each direction with the different radii. The type is '
'either an int (e.g. --roi_radii 10) or an array-like (3,) '
'(e.g. --roi_radii 20 30 10). [%(default)s]')
p.add_argument(
'--roi_center', metavar='tuple(3)', nargs=3, type=int,
help='If supplied, use this center to span the cuboid roi '
'using roi_radii. [center of the 3D volume] '
'(e.g. --roi_center 66 79 79)')
p.add_argument(
'--wm_frf_mask', metavar='file', default='',
help='Path to the output WM frf mask file, the voxels used '
'to compute the WM frf.')
p.add_argument(
'--gm_frf_mask', metavar='file', default='',
help='Path to the output GM frf mask file, the voxels used '
'to compute the GM frf.')
p.add_argument(
'--csf_frf_mask', metavar='file', default='',
help='Path to the output CSF frf mask file, the voxels used '
'to compute the CSF frf.')
p.add_argument(
'--frf_table', metavar='file', default='',
help='Path to the output frf table file. Saves the frf for '
'each b-value, in .txt format.')
add_force_b0_arg(p)
add_overwrite_arg(p)
add_verbose_arg(p)
return p
