[Neuroimaging] [DIPY] propagator anisotropy estimation using MAP(L)MRI

Rutger Fick fick.rutger at gmail.com
Fri Jan 12 18:24:33 EST 2018


Hi Ping,

Attached is the mapmri code that also has PA, just put it in the
dipy/reconst/ folder (where also the current mapmri.py file is) and run
"python setup.py install" from dipy's main folder. That should make it
usable in the same way as the current mapmri module.
Note that its based on an old implementation that still works with the
"cvxopt" optimizer package, so you'll have to install cvxopt to make it run.

I recommend you use the model with both laplacian regularization and
positivity constraint, this give the best results in general.

from dipy.reconst import mapmri_pa
mapmod = mapmri_pa.MapmriModel(gtab,
                               laplacian_regularization=True,  # this
regularization enhances reproducibility of estimated q-space indices by
imposing smoothness
                               laplacian_weighting="GCV",  # this makes it
use generalized cross-validation to find the best regularization weight
                               positivity_constraint=True)  # this ensures
the estimated PDF is positive
mapfit = mapmod.fit(data)
pa = mapfit.pa()

Aside from the original MAPMRI citation for Ozarslan et al. (2013), note
that the relevant citation for dipy's laplacian-regularized MAP-MRI
implementation is [1].
[1] Fick, Rutger HJ, et al. "MAPL: Tissue microstructure estimation using
Laplacian-regularized MAP-MRI and its application to HCP data." *NeuroImage*
134 (2016): 365-385.

Hope it helps and let me know if you need anything else,
Rutger


On 12 January 2018 at 21:48, Ping-Hong Yeh <pinghongyeh at gmail.com> wrote:

> Hi Roger,
>
> Thanks for the prompt reply.
> May I have the code for estimating PA?
>
> Ping
>
> On Jan 12, 2018 3:21 PM, "Rutger Fick" <fick.rutger at gmail.com> wrote:
>
>> Hi Ping,
>>
>> MAPL is just a name for using laplacian-regularized MAP-MRI. If you're
>> using the dipy mapmri implementation then you're using MAPL by default.
>> From a fitted mapmri model you can estimate overall non-gaussianity using
>> fitted_model.ng(), and parallel and perpendicular non-Gaussianity using
>> ng_parallel() and ng_perpendicperpendicularular().
>> Propagator Anisotropic is not included in the current dipy
>> implementation. However, I do have a personal version of dipy's mapmri
>> implementation that includes it, if you're interested.
>>
>> Best,
>> Rutger
>>
>> On 12 January 2018 at 16:49, Ping-Hong Yeh <pinghongyeh at gmail.com> wrote:
>>
>>> Hi DIPY users,
>>>
>>> I would like to know the way of estimating non-Gaussian and PA,
>>> mentioned in the Avram et al. “Clinical feasibility of using mean
>>> apparent propagator (MAP) MRI to characterize brain tissue microstructure”
>>> paper,  using MAPMRI or MAPL model.
>>>
>>> Thank you.
>>>
>>> Ping
>>>
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>>>
>>
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