[Neuroimaging] deterministic tracking with TensorModel in dipy

[Nico Hoffmann]

Dear all,

I’m currently implementing certain tractography workflows using dipy and I got spherical harmonics, constrained spherical deconvolution, etc. workflows up and running. However, I’m struggling with the simplest one - a single tensor model (DTI) workflow. I just can't figure out how to track fibres just using this model without any spherical harmonics, ... There are lots of tutorials explaining fibre tracking with quite complex models, yet these approaches (mostly utilizing peaks_from_model(..) ) don’t seem to be useful in case of a single tensor model.


My current workflow is:

[…]
import dipy.reconst.dti as dti

ccseeds = seeds_from_mask(ccmask, affine=aff)
binary_classifier = BinaryTissueClassifier(binarymask == 1)

start_time = time.time()
dti_wls = dti.TensorModel(gtab_subset)
fit_wls = dti_wls.fit(dwi_subset)
runtime = time.time() - start_time
print('Runtime ' + str(runtime) + 's’)

???

streamlines_generator = LocalTracking(???, binary_classifier, ccseeds, aff, step_size=.1)
streamlines = Streamlines(streamlines_generator)


I’d really appreciate any help!


Thank you,
Nico
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