Code: http://github.com/nipy/nipype/blob/master/nipype/workflows/dmri/fsl/dti.py#L13
Creates a pipeline that does the same as bedpostx script from FSL - calculates diffusion model parameters (distributions not MLE) voxelwise for the whole volume (by splitting it slicewise).
>>> nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
>>> nipype_bedpostx.inputs.inputnode.dwi = 'diffusion.nii'
>>> nipype_bedpostx.inputs.inputnode.mask = 'mask.nii'
>>> nipype_bedpostx.inputs.inputnode.bvecs = 'bvecs'
>>> nipype_bedpostx.inputs.inputnode.bvals = 'bvals'
>>> nipype_bedpostx.inputs.xfibres.n_fibres = 2
>>> nipype_bedpostx.inputs.xfibres.fudge = 1
>>> nipype_bedpostx.inputs.xfibres.burn_in = 1000
>>> nipype_bedpostx.inputs.xfibres.n_jumps = 1250
>>> nipype_bedpostx.inputs.xfibres.sample_every = 25
>>> nipype_bedpostx.run()
Inputs:
inputnode.dwi
inputnode.mask
Outputs:
outputnode.thsamples
outputnode.phsamples
outputnode.fsamples
outputnode.mean_thsamples
outputnode.mean_phsamples
outputnode.mean_fsamples
outputnode.dyads
outputnode.dyads_dispersion
Code: http://github.com/nipy/nipype/blob/master/nipype/workflows/dmri/fsl/dti.py#L169
Creates a pipeline that replaces eddy_correct script in FSL. It takes a series of diffusion weighted images and linearly corregisters them to one reference image.
>>> nipype_eddycorrect = create_eddy_correct_pipeline("nipype_eddycorrect")
>>> nipype_eddycorrect.inputs.inputnode.in_file = 'diffusion.nii'
>>> nipype_eddycorrect.inputs.inputnode.ref_num = 0
>>> nipype_eddycorrect.run()
Inputs:
inputnode.in_file
inputnode.ref_num
Outputs:
outputnode.eddy_corrected