def get_parser():
parser = SCTArgumentParser(
description=
"Flatten the spinal cord such within the medial sagittal plane. Useful to make nice pictures. "
"Output data has suffix _flatten. Output type is float32 (regardless of input type) to minimize "
"loss of precision during conversion.")
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument('-i',
metavar=Metavar.file,
required=True,
help="Input volume. Example: t2.nii.gz")
mandatory.add_argument(
'-s',
metavar=Metavar.file,
required=True,
help="Spinal cord segmentation or centerline. Example: t2_seg.nii.gz")
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description="Compute temporal SNR (tSNR) in fMRI time series.")
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument('-i',
metavar=Metavar.file,
required=True,
help="Input fMRI data. Example: fmri.nii.gz")
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
optional.add_argument(
'-o',
metavar=Metavar.file,
help="tSNR data output file. Example: fmri_tsnr.nii.gz")
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Display scatter plot of gradient directions from bvecs file.')
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
'-bvec',
metavar=Metavar.file,
required=True,
help="Input bvecs file. Example: bvecs.txt",
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description='Concatenate bval files in time.'
)
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
"-i",
nargs='+',
required=True,
help='List of the bval files to concatenate. Example: dmri_b700.bval dmri_b2000.bval',
metavar=Metavar.file,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument(
"-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-o",
help='Output file with bvals merged. Example: dmri_b700_b2000_concat.bval',
metavar=Metavar.file)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help="Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode")
return parser
def get_parser():
parser = SCTArgumentParser(
description='Transpose bvecs file (if necessary) to get nx3 structure.'
)
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument('-bvec',
metavar=Metavar.file,
required=True,
help="Input bvecs file. Example: bvecs.txt")
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
optional.add_argument(
'-o',
metavar=Metavar.file,
default='',
help=
"Output bvecs file. By default, input file is overwritten. Example: bvecs_t.txt"
)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Compute SNR using methods described in [Dietrich et al., Measurement of'
' signal-to-noise ratios in MR images: Influence of multichannel coils, parallel '
'imaging, and reconstruction filters. J Magn Reson Imaging 2007; 26(2): 375-385].'
)
mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatoryArguments.add_argument(
'-i',
required=True,
help=
'4D data to compute the SNR on (along the 4th dimension). Example: b0s.nii.gz',
metavar=Metavar.file)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
'-m',
help=
'Binary (or weighted) mask within which SNR will be averaged. Example: dwi_moco_mean_seg.nii.gz',
metavar=Metavar.file,
default='')
optional.add_argument(
'-method',
help='R|Method to use to compute the SNR:\n'
' diff (default): Substract two volumes (defined by -vol) and estimate noise variance within the ROI (flag -m is required).\n'
' mult: Estimate noise variance over time across volumes specified with -vol.',
choices=('diff', 'mult'),
default='diff')
optional.add_argument(
'-vol',
help=
'Volumes to compute SNR from. Separate with "," (Example: -vol 0,1), or select range '
'using ":" (Example: -vol 2:50). By default, all volumes in series are selected.',
metavar=Metavar.str,
default='')
optional.add_argument('-r',
type=int,
help='Remove temporary files.',
default=1,
choices=(0, 1))
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Compute Maximum Spinal Cord Compression (MSCC) as in: Miyanji F, Furlan JC, Aarabi B, Arnold PM, '
'Fehlings MG. Acute cervical traumatic spinal cord injury: MR imaging findings correlated with '
'neurologic outcome--prospective study with 100 consecutive patients. Radiology 2007;243(3):820-'
'827.')
mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatoryArguments.add_argument(
'-di',
type=float,
required=True,
help=
'Anteroposterior cord distance (in mm) at the level of maximum injury. Example: 6.85',
metavar=Metavar.float,
)
mandatoryArguments.add_argument(
'-da',
type=float,
required=True,
help=
'Anteroposterior cord distance (in mm) at the nearest normal level above the level of injury.',
metavar=Metavar.float,
)
mandatoryArguments.add_argument(
'-db',
type=float,
required=True,
help=
'Anteroposterior cord distance (in mm) at the nearest normal level below the level of injury.',
metavar=Metavar.float,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description='Compute magnetization transfer ratio (MTR). Output is given in percentage.'
)
mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatoryArguments.add_argument(
'-mt0',
required=True,
help='Image without MT pulse (MT0)',
metavar=Metavar.float,
)
mandatoryArguments.add_argument(
'-mt1',
required=True,
help='Image with MT pulse (MT1)',
metavar=Metavar.float,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument(
"-thr",
type=float,
help="Threshold to clip MTR output values in case of division by small number. This implies that the output image"
"range will be [-thr, +thr]. Default: 100.",
default=100
)
optional.add_argument(
"-h",
"--help",
action="help",
help="Show this help message and exit"
)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help="Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
optional.add_argument(
'-o',
help='Path to output file.',
metavar=Metavar.str,
default=os.path.join('.', 'mtr.nii.gz')
)
return parser
def get_parser():
parser = SCTArgumentParser(description='Calculate b-value (in mm^2/s).')
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
"-g",
type=float,
required=True,
help="Amplitude of diffusion gradients (in mT/m). Example: 40",
metavar=Metavar.float,
)
mandatory.add_argument(
"-b",
type=float,
required=True,
help=
"Big delta: time between both diffusion gradients (in ms). Example: 40",
metavar=Metavar.float,
)
mandatory.add_argument(
"-d",
type=float,
required=True,
help=
"Small delta: duration of each diffusion gradient (in ms). Example: 30",
metavar=Metavar.float,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Display scatter plot of gradient directions from bvecs file.')
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
'-bvec',
metavar=Metavar.file,
required=True,
help="Input bvecs file. Example: bvecs.txt",
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
return parser
def get_parser():
parser = SCTArgumentParser(
description='Convert image file to another type.')
mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatoryArguments.add_argument(
"-i",
required=True,
help='File input. Example: data.nii.gz',
metavar=Metavar.file,
)
mandatoryArguments.add_argument(
"-o",
required=True,
help='File output (indicate new extension). Example: data.nii',
metavar=Metavar.str,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-squeeze",
type=int,
help='Sueeze data dimension (remove unused dimension)',
required=False,
choices=(0, 1),
default=1)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Concatenate bvec files in time. You can either use bvecs in lines or columns. '
'N.B.: Return bvecs in lines. If you need it in columns, please use '
'sct_dmri_transpose_bvecs afterwards.')
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
"-i",
nargs='+',
required=True,
help=
'List of the bvec files to concatenate. Example: dmri_b700.bvec dmri_b2000.bvec',
metavar=Metavar.file,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-o",
metavar=Metavar.file,
help=
'Output file with bvecs concatenated. Example: dmri_b700_b2000_concat.bvec'
)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
# initialize parameters
# TODO: create a class ParamFmriMoco which inheritates from ParamMoco
param_default = ParamMoco(group_size=1, metric='MeanSquares', smooth='0')
# parser initialisation
parser = SCTArgumentParser(
description=
"Motion correction of fMRI data. Some robust features include:\n"
" - group-wise (-g)\n"
" - slice-wise regularized along z using polynomial function (-p)\n"
" (For more info about the method, type: isct_antsSliceRegularizedRegistration)\n"
" - masking (-m)\n"
" - iterative averaging of target volume\n"
"\n"
"The outputs of the motion correction process are:\n"
" - the motion-corrected fMRI volumes\n"
" - the time average of the corrected fMRI volumes\n"
" - a time-series with 1 voxel in the XY plane, for the X and Y motion direction (two separate "
"files), as required for FSL analysis.\n"
" - a TSV file with the slice-wise average of the motion correction for XY (one file), that "
"can be used for Quality Control.\n")
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument('-i',
metavar=Metavar.file,
required=True,
help="Input data (4D). Example: fmri.nii.gz")
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
optional.add_argument(
'-g',
metavar=Metavar.int,
type=int,
help="Group nvols successive fMRI volumes for more robustness.")
optional.add_argument(
'-m',
metavar=Metavar.file,
help=
"Binary mask to limit voxels considered by the registration metric.")
optional.add_argument(
'-param',
metavar=Metavar.list,
type=list_type(',', str),
help=
f"R|Advanced parameters. Assign value with \"=\"; Separate arguments with \",\".\n"
f" - poly [int]: Degree of polynomial function used for regularization along Z. For no regularization "
f"set to 0. Default={param_default.poly}.\n"
f" - smooth [mm]: Smoothing kernel. Default={param_default.smooth}.\n"
f" - iter [int]: Number of iterations. Default={param_default.iter}.\n"
f" - metric {{MI, MeanSquares, CC}}: Metric used for registration. Default={param_default.metric}.\n"
f" - gradStep [float]: Searching step used by registration algorithm. The higher the more deformation "
f"allowed. Default={param_default.gradStep}.\n"
f" - sampling [None or 0-1]: Sampling rate used for registration metric. "
f"Default={param_default.sampling}.\n"
f" - numTarget [int]: Target volume or group (starting with 0). Default={param_default.num_target}.\n"
f" - iterAvg [int]: Iterative averaging: Target volume is a weighted average of the "
f"previously-registered volumes. Default={param_default.iterAvg}.\n")
optional.add_argument('-ofolder',
metavar=Metavar.folder,
action=ActionCreateFolder,
default='./',
help="Output path.")
optional.add_argument('-x',
choices=['nn', 'linear', 'spline'],
default='linear',
help="Final interpolation.")
optional.add_argument('-r',
metavar=Metavar.int,
type=int,
choices=[0, 1],
default=1,
help="Remove temporary files. O = no, 1 = yes")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description='Compute Diffusion Tensor Images (DTI) using dipy.')
mandatory = parser.add_argument_group("MANDATORY ARGMENTS")
mandatory.add_argument(
"-i",
required=True,
help='Input 4d file. Example: dmri.nii.gz',
metavar=Metavar.file,
)
mandatory.add_argument(
"-bval",
required=True,
help='Bvals file. Example: bvals.txt',
metavar=Metavar.file,
)
mandatory.add_argument(
"-bvec",
required=True,
help='Bvecs file. Example: bvecs.txt',
metavar=Metavar.file,
)
optional = parser.add_argument_group("OPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
'-method',
help='Type of method to calculate the diffusion tensor:\n'
' standard: Standard equation [Basser, Biophys J 1994]\n'
' restore: Robust fitting with outlier detection [Chang, MRM 2005]',
default='standard',
choices=('standard', 'restore'))
optional.add_argument("-evecs",
type=int,
help='Output tensor eigenvectors and eigenvalues.',
default=0,
choices=(0, 1))
optional.add_argument(
'-m',
metavar=Metavar.file,
help=
'Mask used to compute DTI in for faster processing. Example: mask.nii.gz'
)
optional.add_argument('-o',
help='Output prefix.',
metavar=Metavar.str,
required=False,
default='dti_')
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description='Utility function to denoise images. (Return the denoised image and also the difference '
'between the input and the output.)'
)
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
"-i",
default=None,
required=True,
help="Input NIFTI image to be denoised. Example: image_input.nii.gz",
metavar=Metavar.file,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument(
"-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-p",
help='Type of assumed noise distribution. Default is: Rician.',
required=False,
choices=("Rician", "Gaussian"),
default="Rician")
optional.add_argument(
"-d",
type=int,
help="Threshold value for what to be considered as noise. "
"The standard deviation of the noise is calculated for values below this limit. "
"Not relevant if -std value is precised. Default is 80.",
metavar=Metavar.int,
required=False,
default="80")
optional.add_argument(
"-std",
type=float,
help="Standard deviation of the noise. "
"If not specified, it is calculated using a background of point of values "
"below the threshold value (parameter d).",
metavar=Metavar.float)
optional.add_argument(
"-o",
help="Name of the output NIFTI image.",
metavar=Metavar.str,
default=None)
optional.add_argument(
"-r",
help="Remove temporary files. Specify 0 to get access to temporary files.",
type=int,
choices=(0, 1),
default=1)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help="Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode")
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Spinal Cord Gray Matter (GM) Segmentation using deep dilated convolutions. The contrast of the '
'input image must be similar to a T2*-weighted image: WM dark, GM bright and CSF bright. '
'Reference: Perone CS, Calabrese E, Cohen-Adad J. Spinal cord gray matter segmentation using deep '
'dilated convolutions. Sci Rep 2018;8(1):5966.')
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
"-i",
required=True,
help="Image filename to segment (3D volume). Example: t2s.nii.gz.",
metavar=Metavar.file)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-o",
help="Output segmentation file name. Example: sc_gm_seg.nii.gz",
metavar=Metavar.file,
default=None)
misc = parser.add_argument_group('\nMISC')
misc.add_argument(
'-qc',
help=
"The path where the quality control generated content will be saved.",
metavar=Metavar.str,
default=None)
misc.add_argument(
'-qc-dataset',
help=
'If provided, this string will be mentioned in the QC report as the dataset the process was run on',
metavar=Metavar.str)
misc.add_argument(
'-qc-subject',
help=
'If provided, this string will be mentioned in the QC report as the subject the process was run on',
metavar=Metavar.str)
misc.add_argument("-m",
help="Model to use (large or challenge). "
"The model 'large' will be slower but "
"will yield better results. The model "
"'challenge' was built using data from "
"the following challenge: goo.gl/h4AVar.",
choices=('large', 'challenge'),
default='large')
misc.add_argument(
"-thr",
type=float,
help=
'Threshold to apply in the segmentation predictions, use 0 (zero) to disable it. Example: 0.999',
metavar=Metavar.float,
default=0.999)
misc.add_argument("-t",
help="Enable TTA (test-time augmentation). "
"Better results, but takes more time and "
"provides non-deterministic results.",
metavar='')
misc.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description="Tools to crop an image. Either via command line or via a Graphical User Interface (GUI). See "
"example usage at the end.",
epilog="EXAMPLES:\n"
"- To crop an image using the GUI (this does not allow to crop along the right-left dimension):\n"
"sct_crop_image -i t2.nii.gz -g 1\n\n"
"- To crop an image using a binary mask:\n"
"sct_crop_image -i t2.nii.gz -m mask.nii.gz\n\n"
"- To crop an image using a reference image:\n"
"sct_crop_image -i t2.nii.gz -ref mt1.nii.gz\n\n"
"- To crop an image by specifying min/max (you don't need to specify all dimensions). In the example "
"below, cropping will occur between x=5 and x=60, and between z=5 and z=zmax-1\n"
"sct_crop_image -i t2.nii.gz -xmin 5 -xmax 60 -zmin 5 -zmax -2\n\n"
)
mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatoryArguments.add_argument(
'-i',
required=True,
help="Input image. Example: t2.nii.gz",
metavar=Metavar.file,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument(
'-h',
'--help',
action='help',
help="Show this help message and exit")
optional.add_argument(
'-o',
help="Output image. By default, the suffix '_crop' will be added to the input image.",
metavar=Metavar.str,
)
optional.add_argument(
'-g',
type=int,
help="0: Cropping via command line | 1: Cropping via GUI. Has priority over -m.",
choices=(0, 1),
default=0,
)
optional.add_argument(
'-m',
help="Binary mask that will be used to extract bounding box for cropping the image. Has priority over -ref.",
metavar=Metavar.file,
)
optional.add_argument(
'-ref',
help="Image which dimensions (in the physical coordinate system) will be used as a reference to crop the "
"input image. Only works for 3D images. Has priority over min/max method.",
metavar=Metavar.file,
)
optional.add_argument(
'-xmin',
type=int,
default=0,
help="Lower bound for cropping along X.",
metavar=Metavar.int,
)
optional.add_argument(
'-xmax',
type=int,
default=-1,
help="Higher bound for cropping along X. Setting '-1' will crop to the maximum dimension (i.e. no change), "
"'-2' will crop to the maximum dimension minus 1 slice, etc.",
metavar=Metavar.int,
)
optional.add_argument(
'-ymin',
type=int,
default=0,
help="Lower bound for cropping along Y.",
metavar=Metavar.int,
)
optional.add_argument(
'-ymax',
type=int,
default=-1,
help="Higher bound for cropping along Y. Follows the same rules as xmax.",
metavar=Metavar.int,
)
optional.add_argument(
'-zmin',
type=int,
default=0,
help="Lower bound for cropping along Z.",
metavar=Metavar.int,
)
optional.add_argument(
'-zmax',
type=int,
default=-1,
help="Higher bound for cropping along Z. Follows the same rules as xmax.",
metavar=Metavar.int,
)
optional.add_argument(
'-b',
type=int,
default=None,
help="If this flag is declared, the image will not be cropped (i.e. the dimension will not change). Instead, "
"voxels outside the bounding box will be set to the value specified by this flag. For example, to have "
"zeros outside the bounding box, use: '-b 0'",
metavar=Metavar.int,
)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help="Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode")
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Compute SNR using methods described in [Dietrich et al., Measurement of'
' signal-to-noise ratios in MR images: Influence of multichannel coils, parallel '
'imaging, and reconstruction filters. J Magn Reson Imaging 2007; 26(2): 375-385].'
)
mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatoryArguments.add_argument(
'-i',
required=True,
help="Image to compute the SNR on. (Example: b0s.nii.gz)\n"
"- For '-method diff' and '-method mult', the image must be 4D, as SNR will be computed "
"along the 4th dimension.\n"
"- For '-method single', the image can either be 3D or 4D. If a 4D image is passed, a specific "
"3D volume should be specified using the '-vol' argument.",
metavar=Metavar.file)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
'-m',
help=
'Binary (or weighted) mask within which SNR will be averaged. Example: dwi_moco_mean_seg.nii.gz',
metavar=Metavar.file,
default='')
optional.add_argument(
'-m-noise',
help=
"Binary (or weighted) mask within which noise will be calculated. Only valid for '-method single'.",
metavar=Metavar.file,
default='')
optional.add_argument(
'-method',
help='Method to use to compute the SNR (default: diff):\n'
" - diff: Substract two volumes (defined by -vol) and estimate noise variance within the ROI "
"(flag '-m' is required). Requires a 4D volume.\n"
" - mult: Estimate noise variance over time across volumes specified with '-vol'. Requires a 4D volume.\n"
" - single: Compute the mean signal in the mask specified by '-m' and estimate the noise variance in a "
"mask specified by '-m-noise'. If the noise mask is in the background (air), the noise variance needs to "
"be corrected for Rayleigh distribution (set '-rayleigh 1'). If the noise mask is located in a "
"region with high signal (eg: tissue), noise distribution can be assumed Gaussian and there is no need to "
"correct for Rayleigh distribution (use '-rayleigh 0'). This implementation corresponds to the SNRstdv "
"in the Dietrich et al. article. Uses a 3D or a 4D volume. If a 4D volume is input, the volume to "
"compute SNR on is specified by '-vol'.",
choices=('diff', 'mult', 'single'),
default='diff')
optional.add_argument(
'-vol',
help=
"Volumes to compute SNR from. Separate with ',' (Example: '-vol 0,1'), or select range "
"using ':' (Example: '-vol 2:50'). If this argument is not passed:\n"
" - For '-method mult', all volumes will be used.\n"
" - For '-method diff', the first two volumes will be used.\n"
" - For '-method single', the first volume will be used.",
metavar=Metavar.str,
default='')
optional.add_argument(
'-rayleigh',
type=int,
help=
"Correct for Rayleigh distribution. It is recommended to always use this correction for the 'diff' method "
"and to use it with the 'single' method in case the noise mask is taken in a region with low SNR (e.g., "
"the air). ",
default=1,
choices=(0, 1))
optional.add_argument('-r',
type=int,
help='Remove temporary files.',
default=1,
choices=(0, 1))
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
optional.add_argument('-o',
metavar=Metavar.str,
type=str,
default=None,
help="File name to write the computed SNR to.")
return parser
def get_parser():
parser = SCTArgumentParser(
description=
"Concatenate b=0 scans with DWI time series and update the bvecs and bvals files.\n\n"
"Example 1: Add two b=0 file at the beginning and one at the end of the DWI time series:\n"
">> sct_dmri_concat_b0_and_dwi -i b0-1.nii b0-2.nii dmri.nii b0-65.nii -bvec bvecs.txt -bval "
"bvals.txt -order b0 b0 dwi b0 -o dmri_concat.nii -obval bvals_concat.txt -obvec "
"bvecs_concat.txt\n\n"
"Example 2: Concatenate two DWI series and add one b=0 file at the beginning:\n"
">> sct_dmri_concat_b0_and_dwi -i b0-1.nii dmri1.nii dmri2.nii -bvec bvecs1.txt bvecs2.txt -bval "
"bvals1.txt bvals2.txt -order b0 dwi dwi -o dmri_concat.nii -obval bvals_concat.txt -obvec "
"bvecs_concat.txt")
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
'-i',
nargs='+',
required=True,
help=
"Input 4d files, separated by space, listed in the right order of concatenation. Example: b0.nii dmri1.nii dmri2.nii",
metavar=Metavar.file,
)
mandatory.add_argument(
'-bval',
nargs='+',
required=True,
help="Bvals file(s). Example: bvals.txt",
metavar=Metavar.file,
)
mandatory.add_argument(
'-bvec',
nargs='+',
required=True,
help="Bvecs file(s). Example: bvecs.txt",
metavar=Metavar.file,
)
mandatory.add_argument(
'-order',
nargs='+',
required=True,
help=
"Order of b=0 and DWI files entered in flag '-i', separated by space. Example: b0 dwi dwi",
choices=['b0', 'dwi'],
)
mandatory.add_argument(
'-o',
required=True,
help="Output 4d concatenated file. Example: b0_dmri_concat.nii",
metavar=Metavar.file,
)
mandatory.add_argument(
'-obval',
required=True,
help="Output concatenated bval file. Example: bval_concat.txt",
metavar=Metavar.file,
)
mandatory.add_argument(
'-obvec',
required=True,
help="Output concatenated bvec file. Example: bvec_concat.txt",
metavar=Metavar.file,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument(
'-h',
'--help',
action='help',
help="Show this help message and exit",
)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description="Anisotropic resampling of 3D or 4D data.")
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
'-i',
metavar=Metavar.file,
required=True,
help=
"Image to segment. Can be 3D or 4D. (Cannot be 2D) Example: dwi.nii.gz"
)
resample_types = parser.add_argument_group(
"\nTYPE OF THE NEW SIZE INPUT: with a factor of resampling, in mm or in number of voxels\n"
"Please choose only one of the 3 options")
resample_types.add_argument(
'-f',
metavar=Metavar.str,
help=
"R|Resampling factor in each dimensions (x,y,z). Separate with 'x'. Example: 0.5x0.5x1\n"
"For 2x upsampling, set to 2. For 2x downsampling set to 0.5")
resample_types.add_argument(
'-mm',
metavar=Metavar.str,
help=
"New resolution in mm. Separate dimension with 'x'. Example: 0.1x0.1x5"
)
resample_types.add_argument(
'-vox',
metavar=Metavar.str,
help=
"Resampling size in number of voxels in each dimensions (x,y,z). Separate with 'x'."
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
optional.add_argument(
'-ref',
metavar=Metavar.file,
help=
"Reference image to resample input image to. Uses world coordinates.")
optional.add_argument('-x',
choices=['nn', 'linear', 'spline'],
default='linear',
help="Interpolation method.")
optional.add_argument(
'-o',
metavar=Metavar.file,
help="Output file name. Example: dwi_resampled.nii.gz")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description='Generate Quality Control (QC) report following SCT processing.',
epilog='Examples:\n'
'sct_qc -i t2.nii.gz -s t2_seg.nii.gz -p sct_deepseg_sc\n'
'sct_qc -i t2.nii.gz -s t2_seg_labeled.nii.gz -p sct_label_vertebrae\n'
'sct_qc -i t2.nii.gz -s t2_seg.nii.gz -p sct_deepseg_sc -qc-dataset mydata -qc-subject sub-45'
)
parser.add_argument('-i',
metavar='IMAGE',
help='Input image #1 (mandatory)',
required=True)
parser.add_argument('-p',
help='SCT function associated with the QC report to generate',
choices=('sct_propseg', 'sct_deepseg_sc', 'sct_deepseg_gm', 'sct_register_multimodal',
'sct_register_to_template', 'sct_warp_template', 'sct_label_vertebrae',
'sct_detect_pmj', 'sct_label_utils', 'sct_get_centerline', 'sct_fmri_moco',
'sct_dmri_moco'),
required=True)
parser.add_argument('-s',
metavar='SEG',
help='Input segmentation or label',
required=False)
parser.add_argument('-d',
metavar='DEST',
help='Input image #2 to overlay on image #1 (requires a segmentation), or output of another '
'process (e.g., sct_straighten_spinalcord)',
required=False)
parser.add_argument('-qc',
metavar='QC',
help='Path to save QC report. Default: ./qc',
required=False,
default=os.path.join('.', 'qc'))
parser.add_argument('-qc-dataset',
metavar='DATASET',
help='If provided, this string will be mentioned in the QC report as the dataset the process '
'was run on',
required=False)
parser.add_argument('-qc-subject',
metavar='SUBJECT',
help='If provided, this string will be mentioned in the QC report as the subject the process '
'was run on',
required=False)
parser.add_argument('-fps',
metavar='float',
type=float,
help='The number of frames per second for output gif images. Only useful for sct_fmri_moco and '
'sct_dmri_moco.',
required=False)
parser.add_argument('-v',
action='store_true',
help="Verbose")
parser.add_argument('-h',
'--help',
action="help",
help="show this message and exit")
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Utility function to denoise diffusion MRI images. Return the denoised image and also the difference '
'between the input and the output.The Patch2Self denoising algorithm is based on self-supervised denoising via statistical independence of noise, as described in the following publications:\n'
'\n'
'- Fadnavis et al. Patch2Self: Denoising Diffusion MRI with Self-supervised Learning. NeurIPS, 2020, Vol. 33. (https://arxiv.org/abs/2011.01355)\n'
'- Schilling et al. Patch2Self denoising of diffusion MRI in the cervical spinal cord improves intra-cord contrast, '
'signal modelling, repeatability, and feature conspicuity. medRxiv, 2021. (https://doi.org/10.1101/2021.10.04.21264389)\n'
'\n'
'The implementation is based on DIPY (https://dipy.org/documentation/1.4.1./examples_built/denoise_patch2self/#example-denoise-patch2self)'
)
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
"-i",
default=None,
required=True,
help="Input NIfTI image to be denoised. Example: image_input.nii.gz",
metavar=Metavar.file,
)
mandatory.add_argument(
"-b",
default=None,
required=True,
help="Input bvals file corresponding to the NIfTI file to be denoised."
" Example: filename.bval",
metavar=Metavar.file,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-model",
help=
'Type of regression model used for self-supervised training within Patch2Self.',
required=False,
choices=("ols", "ridge", "lasso"),
default="ols")
optional.add_argument(
"-radius",
help=
"Patch Radius used to generate p-neighbourhoods within Patch2Self. Notes:\n"
"- A radius of '0' will use 1x1x1 p-neighbourhoods, a radius of '1' will use "
"3x3x3 p-neighbourhoods, and so on.\n"
"- For ansiotropic patch sizes, provide a comma-delimited list of 3 integers. "
"(e.g. '-radius 0,1,0'). For isotropic patch sizes, provide a single int value "
"(e.g. '-radius 0').",
metavar=Metavar.int,
required=False,
default="0")
optional.add_argument("-o",
help="Name of the output NIFTI image.",
metavar=Metavar.str,
default=None)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
# initialize parameters
param_default = ParamMoco(is_diffusion=True,
group_size=3,
metric='MI',
smooth='1')
parser = SCTArgumentParser(
description=
"Motion correction of dMRI data. Some of the features to improve robustness were proposed in Xu et "
"al. (http://dx.doi.org/10.1016/j.neuroimage.2012.11.014) and include:\n"
" - group-wise (-g)\n"
" - slice-wise regularized along z using polynomial function (-param). For more info about the "
"method, type: isct_antsSliceRegularizedRegistration\n"
" - masking (-m)\n"
" - iterative averaging of target volume\n")
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument('-i',
metavar=Metavar.file,
required=True,
help="Diffusion data. Example: dmri.nii.gz")
mandatory.add_argument('-bvec',
metavar=Metavar.file,
required=True,
help='Bvecs file. Example: bvecs.txt')
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit.")
optional.add_argument(
'-bval',
metavar=Metavar.file,
default=param_default.fname_bvals,
help='Bvals file. Example: bvals.nii.gz',
)
optional.add_argument(
'-bvalmin',
type=float,
metavar=Metavar.float,
default=param_default.bval_min,
help=
'B-value threshold (in s/mm2) below which data is considered as b=0. Example: 50.0',
)
optional.add_argument(
'-g',
type=int,
metavar=Metavar.int,
default=param_default.group_size,
help=
'Group nvols successive dMRI volumes for more robustness. Example: 2',
)
optional.add_argument(
'-m',
metavar=Metavar.file,
default=param_default.fname_mask,
help=
'Binary mask to limit voxels considered by the registration metric. Example: dmri_mask.nii.gz',
)
optional.add_argument(
'-param',
metavar=Metavar.list,
type=list_type(',', str),
help=
f"R|Advanced parameters. Assign value with \"=\", and separate arguments with \",\".\n"
f" - poly [int]: Degree of polynomial function used for regularization along Z. For no regularization "
f"set to 0. Default={param_default.poly}.\n"
f" - smooth [mm]: Smoothing kernel. Default={param_default.smooth}.\n"
f" - metric {{MI, MeanSquares, CC}}: Metric used for registration. Default={param_default.metric}.\n"
f" - gradStep [float]: Searching step used by registration algorithm. The higher the more deformation "
f"allowed. Default={param_default.gradStep}.\n"
f" - sample [None or 0-1]: Sampling rate used for registration metric. "
f"Default={param_default.sampling}.\n")
optional.add_argument('-x',
choices=['nn', 'linear', 'spline'],
default=param_default.interp,
help="Final interpolation.")
optional.add_argument('-ofolder',
metavar=Metavar.folder,
action=ActionCreateFolder,
default=param_default.path_out,
help="Output folder. Example: dmri_moco_results/")
optional.add_argument("-r",
choices=('0', '1'),
default=param_default.remove_temp_files,
help="Remove temporary files. 0 = no, 1 = yes")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
param_default = Param()
parser = SCTArgumentParser(
description=
"Crash and integrity testing for functions of the Spinal Cord Toolbox. Internet connection is required for downloading testing data.",
)
parser.add_argument(
"--function",
"-f",
help="Test this specific script (eg. 'sct_propseg').",
nargs="+",
)
def arg_jobs(s):
jobs = int(s)
if jobs > 0:
pass
elif jobs == 0:
jobs = multiprocessing.cpu_count()
else:
raise ValueError()
return jobs
parser.add_argument(
"--download",
"-d",
action="store_true",
default=param_default.download,
)
parser.add_argument(
"--path",
"-p",
help='Path to testing data. NB: no need to set if using "-d 1"',
default=param_default.path_data,
)
parser.add_argument(
"--remove-temps",
"-r",
help='Remove temporary files.',
action="store_true",
default=param_default.remove_tmp_file,
)
parser.add_argument(
"--jobs",
"-j",
type=arg_jobs,
help=
"# of simultaneous tests to run (jobs). 0 or unspecified means # of available CPU threads ({})"
.format(multiprocessing.cpu_count()),
default=arg_jobs(0),
)
parser.add_argument(
"--verbose",
"-v",
action="store_true",
default=param_default.verbose,
)
parser.add_argument(
"--abort-on-failure",
help=
"Instead of iterating through all tests, abort at the first one that would fail.",
action="store_true",
)
parser.add_argument(
"--continue-from",
help=
"Instead of running all tests (or those specified by --function, start from this one",
)
parser.add_argument(
"--check-filesystem",
help="Check filesystem for unwanted modifications",
action="store_true",
)
parser.add_argument(
"--execution-folder",
help="Folder where to run tests from (default. temporary)",
)
parser.add_argument('-h',
"--help",
help="show this message and exit",
action="help")
return parser
def get_parser():
parser = SCTArgumentParser(
description='Compute MTsat and T1map. '
'Reference: Helms G, Dathe H, Kallenberg K, Dechent P. High-resolution maps of magnetization '
'transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH '
'MRI. Magn Reson Med 2008;60(6):1396-1407.')
mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatoryArguments.add_argument(
"-mt",
required=True,
help="Image with MT_ON",
metavar=Metavar.file,
)
mandatoryArguments.add_argument(
"-pd",
required=True,
help="Image PD weighted (typically, the MT_OFF)",
metavar=Metavar.file,
)
mandatoryArguments.add_argument(
"-t1",
required=True,
help="Image T1-weighted",
metavar=Metavar.file,
)
optional = parser.add_argument_group('\nOPTIONAL ARGUMENTS')
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-trmt",
help=
"TR [in ms] for mt image. By default, will be fetch from the json sidecar (if it exists).",
type=float,
metavar=Metavar.float,
)
optional.add_argument(
"-trpd",
help=
"TR [in ms] for pd image. By default, will be fetch from the json sidecar (if it exists).",
type=float,
metavar=Metavar.float,
)
optional.add_argument(
"-trt1",
help=
"TR [in ms] for t1 image. By default, will be fetch from the json sidecar (if it exists).",
type=float,
metavar=Metavar.float,
)
optional.add_argument(
"-famt",
help=
"Flip angle [in deg] for mt image. By default, will be fetch from the json sidecar (if it exists).",
type=float,
metavar=Metavar.float,
)
optional.add_argument(
"-fapd",
help=
"Flip angle [in deg] for pd image. By default, will be fetch from the json sidecar (if it exists).",
type=float,
metavar=Metavar.float,
)
optional.add_argument(
"-fat1",
help=
"Flip angle [in deg] for t1 image. By default, will be fetch from the json sidecar (if it exists).",
type=float,
metavar=Metavar.float,
)
optional.add_argument("-b1map",
help="B1 map",
metavar=Metavar.file,
default=None)
optional.add_argument("-omtsat",
metavar=Metavar.str,
help="Output file for MTsat",
default="mtsat.nii.gz")
optional.add_argument("-ot1map",
metavar=Metavar.str,
help="Output file for T1map",
default="t1map.nii.gz")
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
def get_parser():
parser = SCTArgumentParser(
description="Utility functions for label images."
)
req_group = parser.add_argument_group("\nREQUIRED I/O")
req_group.add_argument(
'-i',
metavar=Metavar.file,
required=True,
help="Input image (Required) Example: t2_labels.nii.gz"
)
io_group = parser.add_argument_group("\nOPTIONAL I/O")
io_group.add_argument(
'-o',
metavar=Metavar.file,
default='labels.nii.gz',
help=("Output image. Note: Only some label utilities create an output image. Example: t2_labels.nii.gz")
)
io_group.add_argument(
'-ilabel',
metavar=Metavar.file,
help="File that contain labels that you want to correct. It is possible to add new points with this option. "
"Use with -create-viewer. Example: t2_labels_auto.nii.gz"
)
functions = parser.add_argument_group("\nLABEL FUNCTIONS")
func_group = functions.add_mutually_exclusive_group(required=True)
func_group.add_argument(
'-add',
metavar=Metavar.int,
type=int,
help="Add value to all labels. Value can be negative."
)
func_group.add_argument(
'-create',
metavar=Metavar.list,
type=list_type(':', Coordinate),
help="Create labels in a new image. List labels as: x1,y1,z1,value1:x2,y2,z2,value2. "
"Example: 12,34,32,1:12,35,33,2"
)
func_group.add_argument(
'-create-add',
metavar=Metavar.list,
type=list_type(':', Coordinate),
help="Same as '-create', but add labels to the input image instead of creating a new image. "
"Example: 12,34,32,1:12,35,33,2"
)
func_group.add_argument(
'-create-seg',
metavar=Metavar.list,
type=list_type(':', list_type(',', int)),
help="R|Create labels on a cord segmentation (or centerline) image defined by '-i'. Each label should be "
"specified using the form 'v1,v2' where 'v1' is value of the slice index along the inferior-superior "
"axis, and 'v2' is the value of the label. Separate each label with ':'. \n"
"Example: '-create-seg 5,1:14,2:23,3' adds three labels at the axial slices 5, 14, and 23 (starting from "
"the most inferior slice)."
)
func_group.add_argument(
'-create-seg-mid',
metavar=Metavar.int,
type=int,
help="R|Similar to '-create-seg'. This option takes a single label value, and will automatically select the "
"mid-point slice in the inferior-superior direction (so there is no need for a slice index).\n"
"This is useful for when you have centered the field of view of your data at a specific location. "
"For example, if you already know that the C2-C3 disc is centered in the I-S direction, then "
"you can enter '-create-seg-mid 3' for that label. This saves you the trouble of having to manually "
"specify a slice index using '-create-seg'."
)
func_group.add_argument(
'-create-viewer',
metavar=Metavar.list,
help="Manually label from a GUI a list of labels IDs. Provide a comma-separated list "
"containing individual values and/or intervals. Example: '-create-viewer 1:4,6,8' "
"will allow you to add labels [1,2,3,4,6,8] using the GUI."
)
func_group.add_argument(
'-cubic-to-point',
action="store_true",
help="Compute the center-of-mass for each label value."
)
func_group.add_argument(
'-disc',
metavar=Metavar.file,
help="Create an image with regions labelized depending on values from reference"
)
func_group.add_argument(
'-display',
action="store_true",
help="Display all labels (i.e. non-zero values)."
)
func_group.add_argument(
'-increment',
action="store_true",
help="Takes all non-zero values, sort them along the inverse z direction, and attributes the values "
"1, 2, 3, etc."
)
func_group.add_argument(
'-vert-body',
metavar=Metavar.list,
type=list_type(',', int),
help="R|From vertebral labeling, create points that are centered at the mid-vertebral levels. Separate "
"desired levels with ','. Example: 3,8\n"
"To get all levels, enter 0."
)
func_group.add_argument(
'-vert-continuous',
action="store_true",
help="Convert discrete vertebral labeling to continuous vertebral labeling.",
)
func_group.add_argument(
'-MSE',
metavar=Metavar.file,
help="Compute Mean Square Error between labels from input and reference image. Specify reference image here."
)
func_group.add_argument(
'-remove-reference',
metavar=Metavar.file,
help="Remove labels from input image (-i) that are not in reference image (specified here)."
)
func_group.add_argument(
'-remove-sym',
metavar=Metavar.file,
help="Remove labels from input image (-i) and reference image (specified here) that don't match. You must "
"provide two output names separated by ','."
)
func_group.add_argument(
'-remove',
metavar=Metavar.list,
type=list_type(',', int),
help="Remove labels of specific value (specified here) from reference image."
)
func_group.add_argument(
'-keep',
metavar=Metavar.list,
type=list_type(',', int),
help="Keep labels of specific value (specified here) from reference image."
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument(
"-h",
"--help",
action="help",
help="Show this help message and exit."
)
optional.add_argument(
'-msg',
metavar=Metavar.str,
help="Display a message to explain the labeling task. Use with -create-viewer"
)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help="Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
optional.add_argument(
'-qc',
metavar=Metavar.folder,
action=ActionCreateFolder,
help="The path where the quality control generated content will be saved."
)
optional.add_argument(
'-qc-dataset',
metavar=Metavar.str,
help="If provided, this string will be mentioned in the QC report as the dataset the process was run on."
)
optional.add_argument(
'-qc-subject',
metavar=Metavar.str,
help="If provided, this string will be mentioned in the QC report as the subject the process was run on."
)
return parser
def get_parser():
parser = SCTArgumentParser(
description=
'Utility function to denoise images. Return the denoised image and also the difference '
'between the input and the output. The denoising algorithm is based on the Non-local means'
'methods (Pierrick Coupe, Jose Manjon, Montserrat Robles, Louis Collins. “Adaptive Multiresolution '
'Non-Local Means Filter for 3D MR Image Denoising” IET Image Processing, Institution of '
'Engineering and Technology, 2011). The implementation is based on Dipy (https://dipy.org/).'
)
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
"-i",
default=None,
required=True,
help="Input NIFTI image to be denoised. Example: image_input.nii.gz",
metavar=Metavar.file,
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument("-h",
"--help",
action="help",
help="Show this help message and exit")
optional.add_argument(
"-p",
help='Type of assumed noise distribution. Default is: Rician.',
required=False,
choices=("Rician", "Gaussian"),
default="Rician")
optional.add_argument(
"-d",
type=int,
help="Threshold value for what to be considered as noise. "
"The standard deviation of the noise is calculated for values below this limit. "
"Not relevant if -std value is precised. Default is 80.",
metavar=Metavar.int,
required=False,
default="80")
optional.add_argument(
"-std",
type=float,
help="Standard deviation of the noise. "
"If not specified, it is calculated using a background of point of values "
"below the threshold value (parameter d).",
metavar=Metavar.float)
optional.add_argument("-o",
help="Name of the output NIFTI image.",
metavar=Metavar.str,
default=None)
optional.add_argument(
"-r",
help=
"Remove temporary files. Specify 0 to get access to temporary files.",
type=int,
choices=(0, 1),
default=1)
optional.add_argument(
'-v',
metavar=Metavar.int,
type=int,
choices=[0, 1, 2],
default=1,
# Values [0, 1, 2] map to logging levels [WARNING, INFO, DEBUG], but are also used as "if verbose == #" in API
help=
"Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode"
)
return parser
