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workflows.dmri.fsl.tbss

create_tbss_1_preproc()

Link to code

Preprocess FA data for TBSS: erodes a little and zero end slicers and creates masks(for use in FLIRT & FNIRT from FSL). A pipeline that does the same as tbss_1_preproc script in FSL

Example

>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss1 = tbss.create_tbss_1_preproc()
>>> tbss1.inputs.inputnode.fa_list = ['s1_FA.nii', 's2_FA.nii', 's3_FA.nii']

Inputs:

inputnode.fa_list

Outputs:

outputnode.fa_list
outputnode.mask_list
outputnode.slices

Graph

digraph tbss_1_preproc{

  label="tbss_1_preproc";

  tbss_1_preproc_inputnode[label="inputnode (utility)"];

  tbss_1_preproc_prepfa[label="prepfa (fsl)"];

  tbss_1_preproc_getmask1[label="getmask1 (fsl)"];

  tbss_1_preproc_getmask2[label="getmask2 (fsl)"];

  tbss_1_preproc_slicer[label="slicer (fsl)"];

  tbss_1_preproc_outputnode[label="outputnode (utility)"];

  tbss_1_preproc_inputnode -> tbss_1_preproc_prepfa;

  tbss_1_preproc_inputnode -> tbss_1_preproc_prepfa;

  tbss_1_preproc_prepfa -> tbss_1_preproc_slicer;

  tbss_1_preproc_prepfa -> tbss_1_preproc_outputnode;

  tbss_1_preproc_prepfa -> tbss_1_preproc_getmask1;

  tbss_1_preproc_getmask1 -> tbss_1_preproc_getmask2;

  tbss_1_preproc_getmask1 -> tbss_1_preproc_getmask2;

  tbss_1_preproc_getmask2 -> tbss_1_preproc_outputnode;

  tbss_1_preproc_slicer -> tbss_1_preproc_outputnode;

}

create_tbss_4_prestats()

Link to code

Post-registration processing:Creating skeleton mask using a threshold
projecting all FA data onto skeleton.

A pipeline that does the same as tbss_4_prestats script from FSL

Example

>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss4 = tbss.create_tbss_4_prestats(name='tbss4')
>>> tbss4.inputs.inputnode.skeleton_thresh = 0.2

Inputs:

inputnode.skeleton_thresh
inputnode.groupmask
inputnode.skeleton_file
inputnode.meanfa_file
inputnode.mergefa_file

Outputs:

outputnode.all_FA_skeletonised
outputnode.mean_FA_skeleton_mask
outputnode.distance_map
outputnode.skeleton_file

Graph

digraph tbss_4_prestats{

  label="tbss_4_prestats";

  tbss_4_prestats_inputnode[label="inputnode (utility)"];

  tbss_4_prestats_skeletonmask[label="skeletonmask (fsl)"];

  tbss_4_prestats_invertmask[label="invertmask (fsl)"];

  tbss_4_prestats_distancemap[label="distancemap (fsl)"];

  tbss_4_prestats_projectfa[label="projectfa (fsl)"];

  tbss_4_prestats_outputnode[label="outputnode (utility)"];

  tbss_4_prestats_inputnode -> tbss_4_prestats_invertmask;

  tbss_4_prestats_inputnode -> tbss_4_prestats_skeletonmask;

  tbss_4_prestats_inputnode -> tbss_4_prestats_skeletonmask;

  tbss_4_prestats_inputnode -> tbss_4_prestats_projectfa;

  tbss_4_prestats_inputnode -> tbss_4_prestats_projectfa;

  tbss_4_prestats_inputnode -> tbss_4_prestats_projectfa;

  tbss_4_prestats_skeletonmask -> tbss_4_prestats_invertmask;

  tbss_4_prestats_skeletonmask -> tbss_4_prestats_outputnode;

  tbss_4_prestats_invertmask -> tbss_4_prestats_distancemap;

  tbss_4_prestats_distancemap -> tbss_4_prestats_projectfa;

  tbss_4_prestats_distancemap -> tbss_4_prestats_outputnode;

  tbss_4_prestats_projectfa -> tbss_4_prestats_outputnode;

  tbss_4_prestats_projectfa -> tbss_4_prestats_outputnode;

}

create_tbss_2_reg()

Link to code

TBSS nonlinear registration: A pipeline that does the same as ‘tbss_2_reg -t’ script in FSL. ‘-n’ option is not supported at the moment.

Example

>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss2 = create_tbss_2_reg(name="tbss2")
>>> tbss2.inputs.inputnode.target = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
>>> tbss2.inputs.inputnode.fa_list = ['s1_FA.nii', 's2_FA.nii', 's3_FA.nii']
>>> tbss2.inputs.inputnode.mask_list = ['s1_mask.nii', 's2_mask.nii', 's3_mask.nii']

Inputs:

inputnode.fa_list
inputnode.mask_list
inputnode.target

Outputs:

outputnode.field_list

create_tbss_3_postreg()

Link to code

Post-registration processing: derive mean_FA and mean_FA_skeleton from mean of all subjects in study. Target is assumed to be FMRIB58_FA_1mm. A pipeline that does the same as ‘tbss_3_postreg -S’ script from FSL Setting ‘estimate_skeleton to False will use precomputed FMRIB58_FA-skeleton_1mm skeleton (same as ‘tbss_3_postreg -T’).

Example

>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss3 = tbss.create_tbss_3_postreg()
>>> tbss3.inputs.inputnode.fa_list = ['s1_wrapped_FA.nii', 's2_wrapped_FA.nii', 's3_wrapped_FA.nii']

Inputs:

inputnode.field_list
inputnode.fa_list

Outputs:

outputnode.groupmask
outputnode.skeleton_file
outputnode.meanfa_file
outputnode.mergefa_file

create_tbss_all()

Link to code

Create a pipeline that combines create_tbss_* pipelines

Example

>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss = tbss.create_tbss_all('tbss')
>>> tbss.inputs.inputnode.skeleton_thresh = 0.2

Inputs:

inputnode.fa_list
inputnode.skeleton_thresh

Outputs:

outputnode.meanfa_file
outputnode.projectedfa_file
outputnode.skeleton_file
outputnode.skeleton_mask

create_tbss_non_FA()

Link to code

A pipeline that implement tbss_non_FA in FSL

Example

>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss_MD = tbss.create_tbss_non_FA()
>>> tbss_MD.inputs.inputnode.file_list = []
>>> tbss_MD.inputs.inputnode.field_list = []
>>> tbss_MD.inputs.inputnode.skeleton_thresh = 0.2
>>> tbss_MD.inputs.inputnode.groupmask = './xxx'
>>> tbss_MD.inputs.inputnode.meanfa_file = './xxx'
>>> tbss_MD.inputs.inputnode.distance_map = []

Inputs:

inputnode.file_list
inputnode.field_list
inputnode.skeleton_thresh
inputnode.groupmask
inputnode.meanfa_file
inputnode.distance_map

Outputs:

outputnode.projected_nonFA_file

tbss1_op_string()

Link to code

tbss4_op_string()

Link to code