[Neuroimaging] deterministic tracking with TensorModel in dipy

[Eleftherios Garyfallidis]

The fit_wls object provides the eigen_directions and eigen_values. These
are very similar to the concept of peak_directions and peak_values.
So all you need to do is wrap them in a PeaksAndMetrics object and give it
as input to the local tracker.

Let me know if this is clear.

On Wed, Oct 10, 2018 at 5:06 PM Nico Hoffmann <nico.hoffmann at tu-dresden.de>
wrote:

> 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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