nipype.interfaces.freesurfer.longitudinal module

Provides interfaces to various longitudinal commands provided by freesurfer

FuseSegmentations

Link to code

Bases: FSCommand

Wrapped executable: mri_fuse_segmentations.

fuse segmentations together from multiple timepoints

Examples

>>> from nipype.interfaces.freesurfer import FuseSegmentations
>>> fuse = FuseSegmentations()
>>> fuse.inputs.subject_id = 'tp.long.A.template'
>>> fuse.inputs.timepoints = ['tp1', 'tp2']
>>> fuse.inputs.out_file = 'aseg.fused.mgz'
>>> fuse.inputs.in_segmentations = ['aseg.mgz', 'aseg.mgz']
>>> fuse.inputs.in_segmentations_noCC = ['aseg.mgz', 'aseg.mgz']
>>> fuse.inputs.in_norms = ['norm.mgz', 'norm.mgz', 'norm.mgz']
>>> fuse.cmdline
'mri_fuse_segmentations -n norm.mgz -a aseg.mgz -c aseg.mgz tp.long.A.template tp1 tp2'
Mandatory Inputs:

  • in_norms (a list of items which are a pathlike object or string representing an existing file) – -n <filename> - name of norm file to use (default: norm.mgs) must include the corresponding norm file for all given timepoints as well as for the current subject. Maps to a command-line argument: -n %s.

  • in_segmentations (a list of items which are a pathlike object or string representing an existing file) – Name of aseg file to use (default: aseg.mgz) must include the aseg files for all the given timepoints. Maps to a command-line argument: -a %s.

  • in_segmentations_noCC (a list of items which are a pathlike object or string representing an existing file) – Name of aseg file w/o CC labels (default: aseg.auto_noCCseg.mgz) must include the corresponding file for all the given timepoints. Maps to a command-line argument: -c %s.

  • out_file (a pathlike object or string representing a file) – Output fused segmentation file.

  • timepoints (a list of items which are a string) – Subject_ids or timepoints to be processed. Maps to a command-line argument: %s (position: -2).

Optional Inputs:

  • args (a string) – Additional parameters to the command. Maps to a command-line argument: %s.

  • environ (a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’) – Environment variables. (Nipype default value: {})

  • subject_id (a string) – Subject_id being processed. Maps to a command-line argument: %s (position: -3).

  • subjects_dir (a pathlike object or string representing an existing directory) – Subjects directory.

Outputs:

out_file (a pathlike object or string representing a file) – Output fused segmentation file.

RobustTemplate

Link to code

Bases: FSCommandOpenMP

Wrapped executable: mri_robust_template.

construct an unbiased robust template for longitudinal volumes

Examples

>>> from nipype.interfaces.freesurfer import RobustTemplate
>>> template = RobustTemplate()
>>> template.inputs.in_files = ['structural.nii', 'functional.nii']
>>> template.inputs.auto_detect_sensitivity = True
>>> template.inputs.average_metric = 'mean'
>>> template.inputs.initial_timepoint = 1
>>> template.inputs.fixed_timepoint = True
>>> template.inputs.no_iteration = True
>>> template.inputs.subsample_threshold = 200
>>> template.cmdline  #doctest:
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template mri_robust_template_out.mgz --subsample 200'
>>> template.inputs.out_file = 'T1.nii'
>>> template.cmdline  #doctest:
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --subsample 200'

>>> template.inputs.transform_outputs = ['structural.lta',
...                                      'functional.lta']
>>> template.inputs.scaled_intensity_outputs = ['structural-iscale.txt',
...                                             'functional-iscale.txt']
>>> template.cmdline    
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --iscaleout .../structural-iscale.txt .../functional-iscale.txt --subsample 200 --lta .../structural.lta .../functional.lta'

>>> template.inputs.transform_outputs = True
>>> template.inputs.scaled_intensity_outputs = True
>>> template.cmdline    
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --iscaleout .../is1.txt .../is2.txt --subsample 200 --lta .../tp1.lta .../tp2.lta'

>>> template.run()

References

[https://surfer.nmr.mgh.harvard.edu/fswiki/mri_robust_template]

Mandatory Inputs:

  • auto_detect_sensitivity (a boolean) – Auto-detect good sensitivity (recommended for head or full brain scans). Maps to a command-line argument: --satit. Mutually exclusive with inputs: outlier_sensitivity.

  • in_files (a list of items which are a pathlike object or string representing an existing file) – Input movable volumes to be aligned to common mean/median template. Maps to a command-line argument: --mov %s.

  • out_file (a pathlike object or string representing a file) – Output template volume (final mean/median image). Maps to a command-line argument: --template %s. (Nipype default value: mri_robust_template_out.mgz)

  • outlier_sensitivity (a float) – Set outlier sensitivity manually (e.g. “–sat 4.685” ). Higher values mean less sensitivity. Maps to a command-line argument: --sat %.4f. Mutually exclusive with inputs: auto_detect_sensitivity.

Optional Inputs:

  • args (a string) – Additional parameters to the command. Maps to a command-line argument: %s.

  • average_metric (‘median’ or ‘mean’) – Construct template from: 0 Mean, 1 Median (default). Maps to a command-line argument: --average %d.

  • environ (a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’) – Environment variables. (Nipype default value: {})

  • fixed_timepoint (a boolean) – Map everything to init TP# (init TP is not resampled). Maps to a command-line argument: --fixtp.

  • in_intensity_scales (a list of items which are a pathlike object or string representing an existing file) – Use initial intensity scales. Maps to a command-line argument: --iscalein %s.

  • initial_timepoint (an integer) – Use TP# for special init (default random), 0: no init. Maps to a command-line argument: --inittp %d.

  • initial_transforms (a list of items which are a pathlike object or string representing an existing file) – Use initial transforms (lta) on source. Maps to a command-line argument: --ixforms %s.

  • intensity_scaling (a boolean) – Allow also intensity scaling (default off). Maps to a command-line argument: --iscale.

  • no_iteration (a boolean) – Do not iterate, just create first template. Maps to a command-line argument: --noit.

  • num_threads (an integer) – Allows for specifying more threads.

  • scaled_intensity_outputs (a list of items which are a pathlike object or string representing a file or a boolean) – Final intensity scales (will activate –iscale). Maps to a command-line argument: --iscaleout %s.

  • subjects_dir (a pathlike object or string representing an existing directory) – Subjects directory.

  • subsample_threshold (an integer) – Subsample if dim > # on all axes (default no subs.). Maps to a command-line argument: --subsample %d.

  • transform_outputs (a list of items which are a pathlike object or string representing a file or a boolean) – Output xforms to template (for each input). Maps to a command-line argument: --lta %s.

Outputs:

  • out_file (a pathlike object or string representing an existing file) – Output template volume (final mean/median image).

  • scaled_intensity_outputs (a list of items which are a pathlike object or string representing an existing file) – Output final intensity scales.

  • transform_outputs (a list of items which are a pathlike object or string representing an existing file) – Output xform files from moving to template.