[Neuroimaging] [DIPY] propagator anisotropy estimation using MAP(L)MRI
Rutger Fick
fick.rutger at gmail.com
Wed Jan 17 19:48:26 EST 2018
Hi Ping,
If it's still running and gave only that error then probably it was just a
single voxel that failed and the rest is working. However, I recommend you
first try to fit a smaller dataset (just a few voxels or a single slice)
just to check the results make sense.
I should mention that the code I gave you is slower than Dipy's public
version for reasons I won't get into, so don't worry if you have to wait
longer than before.
Best,
Rutger
On 18 Jan 2018 00:58, "Ping-Hong Yeh" <pinghongyeh at gmail.com> wrote:
> Hi Rutger,
>
> Thanks again for the prompt reply.
>
> Adding "mask" to mapmri have fixed the error; however, another error shows
> up,
>
> mapfit_L = map_model_L.fit(data,mask=data[..., 0]>0)
> dipy/core/geometry.py:129: RuntimeWarning: invalid value encountered in
> true_divide
> theta = np.arccos(z / r)
> dipy/reconst/mapmri_pa.py:364: UserWarning: The MAPMRI positivity
> constraint depends on CVXOPT (http://cvxopt.org/). CVXOPT is licensed
> under the GPL (see: http://cvxopt.org/copyright.html) and you may be
> subject to this license when using the positivity constraint.
> warn(w_s)
> dipy/reconst/mapmri_pa.py:413: UserWarning: Optimization did not find a
> solution
> warn('Optimization did not find a solution')
> Error: Couldn't find per display information
>
>
> It is still running though. Should i stop the running?
>
> Thank you.
> ping
>
> On Tue, Jan 16, 2018 at 7:18 PM, Rutger Fick <fick.rutger at gmail.com>
> wrote:
>
>> Hi Ping,
>>
>> Reading the error messages, it looks like you're fitting a masked voxel.
>> The following error:
>>
>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:389:
>> RuntimeWarning: invalid value encountered in divide
>> data = np.asarray(data / data[self.gtab.b0s_mask].mean())
>>
>> says you're dividing by either zero or NaN, which means your b0 value of
>> that voxel was zero (or you had no b0 values possibly). Note that mapmri
>> needs at least one b0 measurement.
>> I recommend you check if it works when you fit a voxel that you know for
>> sure is in white matter. If it works, you can do something like
>> map_model_L.fit(data, mask=data[..., 0]>0) to use a mask that only fits
>> if the first measured DWI is positive (assuming your first measurement is a
>> b0).
>>
>> Best,
>> Rutger
>>
>>
>>
>> On 16 January 2018 at 23:46, Ping-Hong Yeh <pinghongyeh at gmail.com> wrote:
>>
>>> Hi Rutger,
>>>
>>> I got an error running the map_model.fit using mapmri_pa. Here is the
>>> scripts i used,
>>>
>>>
>>> map_model_L = mapmri_pa.MapmriModel(gtab, radial_order=radial_order,
>>> 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_L = map_model_L.fit(data)
>>>
>>> , and the error message,
>>>
>>>
>>> /Library/Python/2.7/site-packages/dipy/core/geometry.py:129:
>>> RuntimeWarning: invalid value encountered in true_divide
>>> theta = np.arccos(z / r)
>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:364:
>>> UserWarning: The MAPMRI positivity constraint depends on CVXOPT (http:
>>> xopt.org/). CVXOPT is licensed under the GPL (see:
>>> http://cvxopt.org/copyright.html) and you may be subject to this
>>> license when using positivity constraint.
>>> warn(w_s)
>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:389:
>>> RuntimeWarning: invalid value encountered in divide
>>> data = np.asarray(data / data[self.gtab.b0s_mask].mean())
>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:413:
>>> UserWarning: Optimization did not find a solution
>>> warn('Optimization did not find a solution')
>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:444:
>>> UserWarning: Optimization did not find a solution
>>> warn('Optimization did not find a solution')
>>> Traceback (most recent call last):
>>> File "<stdin>", line 1, in <module>
>>> File "/Library/Python/2.7/site-packages/dipy/reconst/multi_voxel.py",
>>> line 33, in new_fit
>>> fit_array[ijk] = single_voxel_fit(self, data[ijk])
>>> File "/Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py",
>>> line 465, in fit
>>> coef_iso = coef_iso / sum(coef_iso * self.Bm_iso)
>>> UnboundLocalError: local variable 'coef_iso' referenced before assignment
>>>
>>>
>>> Any suggestions?
>>>
>>> Thank you.
>>>
>>> ping
>>>
>>> On Fri, Jan 12, 2018 at 6:24 PM, Rutger Fick <fick.rutger at gmail.com>
>>> wrote:
>>>
>>>> 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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