[Neuroimaging] [dipy] Import csd model precomputed by mrtrix

[Ariel Rokem]

On Wed, May 25, 2016 at 4:41 PM, Bago <mrbago at gmail.com> wrote:

> I believe they did change their basis (please correct me if I'm wrong but
> I believe they went from a non-normalized SH basis to a normalized SH
> basis).
>
>
So they have the same basis as dipy now, but the coefficients appear in a
different order? That should make life even easier!


> Also projecting onto a sphere is one way to _estimate_ the coefficients in
> a different basis. The cleaner way is to just re-order the coefficients and
> apply the appropriate scaling. If both basis are normalized (which dipy is)
> the scaling should be 1 or -1.
>
>
Fair point, but to be just a little bit facetious: given enough points on
the sphere and knowledge of the target maximal order of the coefficients,
wouldn't estimating be the same as transforming? Works for the FFT, I
believe :-)

Bago
>
> On Wed, May 25, 2016 at 3:31 PM Ariel Rokem <arokem at gmail.com> wrote:
>
>> On Wed, May 25, 2016 at 1:09 PM, Bago <mrbago at gmail.com> wrote:
>>
>>> Hi Paolo,
>>>   mrtrix and dipy define the SH basis slightly differently, so the
>>> precomputed FOD values need to be adjusted if you want to skip the fit step
>>> and initialize the Fit object directly. IRC we don't currently have the
>>> code to do that, but it would be something we'd like to incorporate.
>>>
>>> Did they change their basis set when they transitioned to mrtrix3? We do
>> have these functions:
>>
>> https://github.com/nipy/dipy/blob/master/dipy/reconst/shm.py#L852-L923
>>
>> That should work with the previous version of mrtrix (mrtrix2?). You can
>> use these to transform between coefficient sets:
>>
>>     sf = sh_to_sf(mrtrix_coeffs, sphere, sh_order, basis_type='mrtrix')
>>     dipy_coeffs = sf_to_sh(sf, sphere, sh_order, basis_type=None) # This
>> defaults to the dipy basis set
>>
>> and then use the CSD model object to predict:
>>
>>     from dipy.reconst.csdeconv import  ConstrainedSphericalDeconvModel
>>     csd_model = ConstrainedSphericalDeconvModel(gtab, response,
>> sh_order=sh_order) # Note: you still need to calculate the response
>> function!
>>     pred_signal = csd_model.predict(dipy_coeffs, gtab, S0)
>>
>> I think that something like this should work (but I haven't tried it
>> myself).
>>
>>
>>> I have a WIP version of the multi-shell CSD model on a separate branch,
>>> I plan on merging it but wasn't intending to get to that for a few months.
>>> If you'd like to look at before then I can push the branch up to github.
>>>
>>> Sounds interesting! I'd love to see what you have so far!
>>
>> Cheers,
>>
>> Ariel
>>
>>
>>> Bago
>>>
>>> On Wed, May 25, 2016 at 2:31 AM Paolo Avesani <avesani at fbk.eu> wrote:
>>>
>>>> I would like to take advantage of the "predict" method of
>>>> reconstruction models in dipy. The goal is to assess the quality of results.
>>>>
>>>> I have already computed the reconstruction models using mrtrix3 and
>>>> stored the ODF files. For this reason I would need to initialize the csd
>>>> model by importing the data from ODF stored by mrtrix3.
>>>>
>>>> The questions are manifold:
>>>> - may I initialize the csd model by providing the precomputed values
>>>> and skipping the "fit" step?
>>>> - may I import the value of precomputed model from a file stored by
>>>> mrtrix3?
>>>> - is the csd model in dipy compliant with the output of multi-shell csd
>>>> model computed by mrtrix3?
>>>>
>>>> I hope my questions and my goal is formulated clearly.
>>>> Thanks for your support.
>>>> Paolo
>>>>
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>>>
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