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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

## `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

### Graph

## `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

### Graph

## `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

## `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

### Graph

## `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

### Graph

## `tbss1_op_string()`

Link to code

## `tbss4_op_string()`

Link to code