def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
# initialization
param = ParamMoco(group_size=1, metric='MeanSquares', smooth='0')
# Fetch user arguments
param.fname_data = arguments.i
param.path_out = arguments.ofolder
param.remove_temp_files = arguments.r
param.interp = arguments.x
if arguments.g is not None:
param.group_size = arguments.g
if arguments.m is not None:
param.fname_mask = arguments.m
if arguments.param is not None:
param.update(arguments.param)
path_qc = arguments.qc
qc_fps = arguments.qc_fps
qc_dataset = arguments.qc_dataset
qc_subject = arguments.qc_subject
qc_seg = arguments.qc_seg
mutually_inclusive_args = (path_qc, qc_seg)
is_qc_none, is_seg_none = [arg is None for arg in mutually_inclusive_args]
if not (is_qc_none == is_seg_none):
raise parser.error(
"Both '-qc' and '-qc-seg' are required in order to generate a QC report."
)
# run moco
fname_output_image = moco_wrapper(param)
set_global_loglevel(
verbose) # moco_wrapper changes verbose to 0, see issue #3341
# QC report
if path_qc is not None:
generate_qc(fname_in1=fname_output_image,
fname_in2=param.fname_data,
fname_seg=qc_seg,
args=sys.argv[1:],
path_qc=os.path.abspath(path_qc),
fps=qc_fps,
dataset=qc_dataset,
subject=qc_subject,
process='sct_fmri_moco')
display_viewer_syntax([fname_output_image, param.fname_data],
mode='ortho,ortho')
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
# initialization
param = ParamMoco(group_size=1, metric='MeanSquares', smooth='0')
# Fetch user arguments
param.fname_data = arguments.i
param.path_out = arguments.ofolder
param.remove_temp_files = arguments.r
param.interp = arguments.x
if arguments.g is not None:
param.group_size = arguments.g
if arguments.m is not None:
param.fname_mask = arguments.m
if arguments.param is not None:
param.update(arguments.param)
# run moco
moco_wrapper(param)
def get_parser():
# initialize parameters
param_default = ParamMoco(is_diffusion=True,
group_size=3,
metric='MI',
smooth='1')
# Initialize the parser
parser = Parser(__file__)
parser.usage.set_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')
parser.add_option(name='-i',
type_value='file',
description='Diffusion data',
mandatory=True,
example='dmri.nii.gz')
parser.add_option(name='-bvec',
type_value='file',
description='Bvecs file',
mandatory=True,
example='bvecs.nii.gz')
parser.add_option(name='-bval',
type_value='file',
description='Bvals file',
mandatory=False,
example='bvals.nii.gz')
parser.add_option(
name='-bvalmin',
type_value='float',
description=
'B-value threshold (in s/mm2) below which data is considered as b=0.',
mandatory=False,
example='50')
parser.add_option(
name='-g',
type_value='int',
description='Group nvols successive dMRI volumes for more robustness.',
mandatory=False,
default_value=param_default.group_size,
example=['2'])
parser.add_option(
name='-m',
type_value='file',
description=
'Binary mask to limit voxels considered by the registration metric.',
mandatory=False,
example=['dmri_mask.nii.gz'])
parser.add_option(
name='-param',
type_value=[[','], 'str'],
description=
"Advanced parameters. Assign value with \"=\"; Separate arguments with \",\"\n"
"poly [int]: Degree of polynomial function used for regularization along Z. For no regularization set to 0. Default="
+ param_default.poly + ".\n"
"smooth [mm]: Smoothing kernel. Default=" + param_default.smooth +
".\n"
"metric {MI, MeanSquares, CC}: Metric used for registration. Default="
+ param_default.metric + ".\n"
"gradStep [float]: Searching step used by registration algorithm. The higher the more deformation allowed. Default="
+ param_default.gradStep + ".\n"
"sample [None or 0-1]: Sampling rate used for registration metric. Default="
+ param_default.sampling + ".\n",
mandatory=False)
parser.add_option(name='-x',
type_value='multiple_choice',
description='Final Interpolation.',
mandatory=False,
default_value=param_default.interp,
example=['nn', 'linear', 'spline'])
parser.add_option(name='-ofolder',
type_value='folder_creation',
description='Output folder',
mandatory=False,
default_value=param_default.path_out,
example='dmri_moco_results/')
parser.usage.addSection('MISC')
parser.add_option(name="-r",
type_value="multiple_choice",
description='Remove temporary files.',
mandatory=False,
default_value='1',
example=['0', '1'])
parser.add_option(
name="-v",
type_value='multiple_choice',
description="verbose: 0 = nothing, 1 = classic, 2 = expended",
mandatory=False,
example=['0', '1', '2'],
default_value='1')
return parser
def main():
# initialization
param = ParamMoco(is_diffusion=True, group_size=3, metric='MI', smooth='1')
# Fetch user arguments
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
param.fname_bvecs = os.path.abspath(arguments['-bvec'])
if '-bval' in arguments:
param.fname_bvals = os.path.abspath(arguments['-bval'])
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
param.path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
param.verbose = int(arguments.get('-v'))
# Update log level
sct.init_sct(log_level=param.verbose, update=True)
# run moco
moco_wrapper(param)
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"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"
)
optional.add_argument(
'-qc',
metavar=Metavar.folder,
action=ActionCreateFolder,
help=
"The path where the quality control generated content will be saved. (Note: "
"Both '-qc' and '-qc-seg' are required in order to generate a QC report.)"
)
optional.add_argument(
'-qc-seg',
metavar=Metavar.file,
help=
"Segmentation of spinal cord to improve cropping in qc report. (Note: "
"Both '-qc' and '-qc-seg' are required in order to generate a QC report.)"
)
optional.add_argument(
'-qc-fps',
metavar=Metavar.float,
type=float,
default=3,
help=
"This float number is the number of frames per second for the output gif images."
)
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 initialisation
parser = Parser(__file__)
# initialize parameters
# TODO: create a class ParamFmriMoco which inheritates from ParamMoco
param_default = ParamMoco(group_size=1, metric='MeanSquares', smooth='0')
parser.usage.set_description(
"""Motion correction of fMRI data. Some robust features include:
- group-wise (-g)
- slice-wise regularized along z using polynomial function (-p)
For more info about the method, type: isct_antsSliceRegularizedRegistration
- masking (-m)
- iterative averaging of target volume
The outputs of the motion correction process are:
- the motion-corrected fMRI volumes
- the time average of the corrected fMRI volumes
- 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.
- a TSV file with the slice-wise average of the motion correction for XY (one file), that can be used for Quality Control."""
)
parser.add_option(name='-i',
type_value='image_nifti',
description='4D data',
mandatory=True,
example='fmri.nii.gz')
parser.add_option(
name='-g',
type_value='int',
description='Group nvols successive fMRI volumes for more robustness.',
mandatory=False,
default_value=param_default.group_size)
parser.add_option(
name='-m',
type_value='image_nifti',
description=
'Binary mask to limit voxels considered by the registration metric.',
mandatory=False)
parser.add_option(
name='-param',
type_value=[[','], 'str'],
description=
"Advanced parameters. Assign value with \"=\"; Separate arguments with \",\"\n"
"poly [int]: Degree of polynomial function used for regularization along Z. For no regularization set to 0. Default="
+ param_default.poly + ".\n"
"smooth [mm]: Smoothing kernel. Default=" + param_default.smooth +
".\n"
"iter [int]: Number of iterations. Default=" + param_default.iter +
".\n"
"metric {MI, MeanSquares, CC}: Metric used for registration. Default="
+ param_default.metric + ".\n"
"gradStep [float]: Searching step used by registration algorithm. The higher the more deformation allowed. Default="
+ param_default.gradStep + ".\n"
"sampling [None or 0-1]: Sampling rate used for registration metric. Default="
+ param_default.sampling + ".\n"
"numTarget [int]: Target volume or group (starting with 0). Default=" +
param_default.num_target + ".\n"
"iterAvg [int]: Iterative averaging: Target volume is a weighted average of the previously-registered volumes. Default="
+ str(param_default.iterAvg) + ".\n",
mandatory=False)
parser.add_option(name='-ofolder',
type_value='folder_creation',
description='Output path.',
mandatory=False,
default_value='./')
parser.add_option(name="-x",
type_value="multiple_choice",
description="""Final interpolation.""",
mandatory=False,
default_value='linear',
example=['nn', 'linear', 'spline'])
parser.add_option(name="-r",
type_value="multiple_choice",
description="""Remove temporary files.""",
mandatory=False,
default_value='1',
example=['0', '1'])
parser.add_option(name="-v",
type_value="multiple_choice",
description="""Verbose.""",
mandatory=False,
default_value='1',
example=['0', '1', '2'])
return parser
def main():
# initialization
param = ParamMoco(group_size=1, metric='MeanSquares', smooth='0')
# Fetch user arguments
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
param.path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
param.verbose = int(arguments.get('-v'))
# Update log level
sct.init_sct(log_level=param.verbose, update=True)
# run moco
moco_wrapper(param)
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 = argparse.ArgumentParser(
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",
formatter_class=SmartFormatter,
add_help=None,
prog=os.path.basename(__file__).strip(".py"))
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',
choices=['0', '1', '2'],
default='1',
help="Verbose: 0 = nothing, 1 = basic, 2 = extended.")
return parser
def main():
# initialization
param = ParamMoco(group_size=1, metric='MeanSquares', smooth='0')
# Fetch user arguments
parser = get_parser()
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
param.fname_data = arguments.i
param.path_out = arguments.ofolder
param.remove_temp_files = arguments.r
param.interp = arguments.x
if arguments.g is not None:
param.group_size = arguments.g
if arguments.m is not None:
param.fname_mask = arguments.m
if arguments.param is not None:
param.update(arguments.param)
param.verbose = int(arguments.v)
# Update log level
init_sct(log_level=param.verbose, update=True)
# run moco
moco_wrapper(param)
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 main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
# initialization
param = ParamMoco(is_diffusion=True, group_size=3, metric='MI', smooth='1')
# Fetch user arguments
param.fname_data = arguments.i
param.fname_bvecs = os.path.abspath(arguments.bvec)
param.fname_bvals = arguments.bval
param.bval_min = arguments.bvalmin
param.group_size = arguments.g
param.fname_mask = arguments.m
param.interp = arguments.x
param.path_out = arguments.ofolder
param.remove_temp_files = arguments.r
if arguments.param is not None:
param.update(arguments.param)
# run moco
moco_wrapper(param)
def main():
# initialization
param = ParamMoco(is_diffusion=True, group_size=3, metric='MI', smooth='1')
# Fetch user arguments
parser = get_parser()
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
param.fname_data = arguments.i
param.fname_bvecs = os.path.abspath(arguments.bvec)
param.fname_bvals = arguments.bval
param.bval_min = arguments.bvalmin
param.group_size = arguments.g
param.fname_mask = arguments.m
param.interp = arguments.x
param.path_out = arguments.ofolder
param.remove_temp_files = arguments.r
if arguments.param is not None:
param.update(arguments.param)
param.verbose = int(arguments.v)
# Update log level
init_sct(log_level=param.verbose, update=True)
# run moco
moco_wrapper(param)