sct_dmri_moco¶
Motion correction of dMRI data. Some of the features to improve robustness were proposed in Xu et al. (https://dx.doi.org/10.1016/j.neuroimage.2012.11.014) and include:
group-wise (
-g)slice-wise regularized along z using polynomial function (
-param poly). For more info about the method, type:isct_antsSliceRegularizedRegistrationmasking (
-m)iterative averaging of target volume
The outputs of the motion correction process are:
the motion-corrected dMRI volumes
the time average of the corrected dMRI volumes with b == 0
the time average of the corrected dMRI volumes with b != 0
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 one row for each time point, with the slice-wise average of the motion correction magnitude for that time point, that can be used for Quality Control.
usage: sct_dmri_moco -i <file> -bvec <file> [-bval <file>] [-bvalmin <float>]
[-g <int>] [-m <file>] [-param <list>]
[-x {nn,linear,spline}] [-ofolder <folder>] [-qc <folder>]
[-qc-seg <file>] [-qc-fps <float>] [-qc-dataset <str>]
[-qc-subject <str>] [-h] [-v <int>] [-r {0,1}]
MANDATORY ARGUMENTS¶
- -i
Diffusion data. Example:
dmri.nii.gz- -bvec
Bvecs file. Example:
bvecs.txt
OPTIONAL ARGUMENTS¶
- -bval
Bvals file. Example:
bvals.txtDefault:
''- -bvalmin
B-value threshold (in s/mm2) below which data is considered as b=0.
Default:
100- -g
Group nvols successive dMRI volumes for more robustness. Values
2or greater will create groups of that size, while a value of1will turn off grouping (i.e. per-volume motion correction).Default:
3- -m
Binary mask to limit voxels considered by the registration metric. You may also provide a softmask (nonbinary, [0, 1]), and it will be binarized at 0.5. Example:
dmri_mask.nii.gzDefault:
''- -param
Advanced parameters. Assign value with
=; Separate arguments with,.poly[int]: Degree of polynomial function used for regularization along Z. For no regularization set to 0. Default=2.smooth[mm]: Smoothing kernel. Default=1.metric{MI, MeanSquares, CC}: Metric used for registration. Default=MI.iter[int]: Number of iterations. Default=10.gradStep[float]: Searching step used by registration algorithm. The higher the more deformation allowed. Default=1.sampling[None or 0-1]: Sampling rate used for registration metric. Default=None.num_target[int]: Target volume or group (starting with 0). Default=0.iterAvg[int]: Iterative averaging: Target volume is a weighted average of the previously-registered volumes. Default=1.
- -x
Possible choices: nn, linear, spline
Final interpolation.
Default:
'spline'- -ofolder
Output folder.
Default:
''- -qc
The path where the quality control generated content will be saved. (Note: Both
-qcand-qc-segare required in order to generate a QC report.)- -qc-seg
Segmentation of spinal cord to improve cropping in qc report. (Note: Both
-qcand-qc-segare required in order to generate a QC report.)- -qc-fps
This float number is the number of frames per second for the output gif images.
Default:
3- -qc-dataset
If provided, this string will be mentioned in the QC report as the dataset the process was run on.
- -qc-subject
If provided, this string will be mentioned in the QC report as the subject the process was run on.
MISC ARGUMENTS¶
- -v
Possible choices: 0, 1, 2
Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode.
Default:
1- -r
Possible choices: 0, 1
Remove temporary files.
Default:
1