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

Ariel Rokem arokem at gmail.com
Fri May 27 12:34:22 EDT 2016 

On Fri, May 27, 2016 at 9:31 AM, Bago <mrbago at gmail.com> wrote:

> You, were spot on, we just needed a transpose :).
>

Ah yes! of course I was :-)




> On Fri, May 27, 2016, 8:44 AM Ariel Rokem <arokem at gmail.com> wrote:
>
>> Hi again,
>>
>> On Fri, May 27, 2016 at 5:23 AM, Ariel Rokem <arokem at gmail.com> wrote:
>>
>>> Hi Paolo,
>>>
>>> Thanks for the update. Sorry to hear it didn't work as hoped.
>>>
>>> On Fri, May 27, 2016 at 2:07 AM, Paolo Avesani <avesani at fbk.eu> wrote:
>>>
>>>> Thanks for your replies.
>>>> A brief update.
>>>>
>>>> Samuel is right. I have in mind to manage the multi-shell model from
>>>> mrtrix3.
>>>> It is clear that up to now it is not viable until the work in progress
>>>> from Bago becomes available.
>>>>
>>>> I tried to follow the example suggested by Ariel, focusing on a single
>>>> shell csd model (coming from dwi2fod command of mrtrix3),
>>>> and setting the sphere as the one used by mrtrix3 (a 300 points). The
>>>> "predict" method raised an issue of disalignment of matrix size.
>>>>
>>>> The issue is at line 217 of csdecon.py when a product between
>>>> "predict_matrix" and "sh_coeff" takes place.
>>>> The "predict matrix" has size (90,45): 90 volumes, 45 sh
>>>> The "sh_coeff" has size (145, 174, 145, 45): the xyz dimensions of the
>>>> volume, 45 sh
>>>>
>>>> It looks the size of predict matrix is wrong. It should be
>>>> (145,174,145,90).
>>>>
>>>>
>>>>
>>> Or the predict_matrix should be (45, 90)? Would be useful if you shared
>>> a minimal example that raises this error.
>>>
>>
>> I was wrong, of course. Does Bago's fix (
>> https://github.com/nipy/dipy/pull/1062) resolve this for you?
>>
>> Cheers,
>>
>> Ariel
>>
>>
>>
>>> Cheers,
>>>
>>> Ariel
>>>
>>>
>>>
>>>>
>>>>
>>>> On Thu, May 26, 2016 at 4:07 AM, Samuel St-Jean <stjeansam at gmail.com>
>>>> wrote:
>>>>
>>>>> Indeed, I thought I read the multitissue version was used here, my
>>>>> mistake as the question does not directly imply that.
>>>>>
>>>>> The regular single shell hopefully does the same (at least I can
>>>>> personally attest the mrtrix2 version and dipy version give similar fodf up
>>>>> to a small rounding factor, but that was before the cholesky decomposition
>>>>> step, so now they should behave the same).
>>>>> On May 26, 2016 09:37, "Bago" <mrbago at gmail.com> wrote:
>>>>>
>>>>>> Samuel mrtrix has at least two models implemented. The multi-shell
>>>>>> (multi-tissue) model cannot be used with single shell data and the original
>>>>>> CSD model cannot be used with multi-shell data.
>>>>>>
>>>>>> Bago
>>>>>>
>>>>>> On Wed, May 25, 2016 at 5:13 PM Samuel St-Jean <stjeansam at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Their wiki explains it, a sqrt(2) to normalize is used in mrtrix3,
>>>>>>> so multiplying your coefficients with that and using the mrtrix2 functions
>>>>>>> should do it.
>>>>>>>
>>>>>>> Although dipy only does single shell, so conclude with consideration
>>>>>>> that the algorithm is different from mrtrix3. Also, csd is a bad signal
>>>>>>> predictor (but good for angle estimation), see the sparc dmri challenge
>>>>>>> paper for example.
>>>>>>> On May 26, 2016 07:55, "Ariel Rokem" <arokem at gmail.com> wrote:
>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> 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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>>>>
>>>>
>>>> --
>>>> -------------------------------------------------------
>>>> Paolo Avesani
>>>> Fondazione Bruno Kessler
>>>> via Sommarive 18,
>>>> 38050 Povo (TN) - I
>>>> phone:   +39 0461 314336
>>>> fax:        +39 0461 302040
>>>> email:     avesani at fbk.eu
>>>> web:       avesani.fbk.eu
>>>>
>>>>
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