[Neuroimaging] [DIPY] propagator anisotropy estimation using MAP(L)MRI
Ping-Hong Yeh
pinghongyeh at gmail.com
Fri Apr 20 10:52:00 EDT 2018
Hi Rutger,
Thank you for the details.
I've just tried the dmipy but hit the ditch for converting bvecs to scheme
file using gtab_dipy2mipy.
Here is the error message,
acq_scheme_mipy = gtab_dipy2mipy(gtab)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File
"/Users/yehp/Downloads/dmipy-master/dmipy/core/acquisition_scheme.py", line
714, in gtab_dipy2mipy
bvalues=bvals, gradient_directions=bvecs, delta=delta, Delta=Delta)
File
"/Users/yehp/Downloads/dmipy-master/dmipy/core/acquisition_scheme.py", line
437, in acquisition_scheme_from_bvalues
check_acquisition_scheme(bvalues, gradient_directions, delta_, Delta_,
TE_)
File
"/Users/yehp/Downloads/dmipy-master/dmipy/core/acquisition_scheme.py", line
696, in check_acquisition_scheme
raise ValueError(msg)
ValueError: gradient orientations n are not unit vectors.
The gtab_dipy2mipy does not like the bvecs i have; it appears that there
are some decimals off. I have applied the norm beforehand using the matlab.
Please find the attached bvecs table.
Ping
On Wed, Apr 11, 2018 at 4:27 AM, Rutger Fick <fick.rutger at gmail.com> wrote:
> Hi Ping,
>
> Clearly the plain GCV is not regularizing sufficiently in the very
> anisotropic areas (e.g. corpus callosum).
> It looks like fixing the regularization weight to 0.2
> (PA_laplacian_weighted0.2 map) is sufficient to fix this problem. Be sure
> to also include positivity.
> Since fixing the weight is also the fastest approach, I suggest you
> proceed to fit both your populations with this approach and see if you are
> able to answer your research questions like this.
> Otherwise, your approach of running GCV with a minimum weight will also
> work, but you'll have to find what minimum weight threshold works for your
> subjects.
>
> Other suggestions:
> - Denoising your data and correcting for the rician noise bias is good
> practice. The current available state-of-the-art MP-PCA approach that I
> know of is in MRtrix: http://mrtrix.readthedocs.io/
> en/latest/dwi_preprocessing/denoising.html
> - If you don't like using MRtrix for some reason, Dipy also has other
> denoising approaches: http://nipy.org/dipy/examples_
> built/denoise_localpca.html
>
> Finally, if you're interested in trying other microstructure estimation
> methods on your data, I suggest you also take a look at our recently
> released "diffusion microstructure imaging in python" (dmipy) package:
> https://github.com/AthenaEPI/dmipy/
> Using dmipy, you can design and fit basically any diffusion microstructure
> model in literature to your data in a few lines of code.
> I suggest you try for example Kaden et al.'s recent Multi-Compartment
> Microscopic Diffusion Imaging with your data, see example
> <http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_multi_compartment_spherical_mean_technique.ipynb>,
> which is very fast to fit as a quick experiment.
>
> Kind regards and let me know how it goes,
> Rutger
>
>
> On 29 March 2018 at 18:02, Ping-Hong Yeh <pinghongyeh at gmail.com> wrote:
>
>> Hi Rutger,
>>
>> We have some bad PA maps created using default settings, and I would like
>> to hear your opinions on improving the fitting.
>>
>> Attached are the screenshots of PA_GCV, norm_laplacian, L_opt and
>> PA_laplacian_weighted0.2 maps.
>> I am currently running the fitting using 0.05 for the minimum bound of
>> the GCV, but I am not sure if that would help.
>>
>> In order to do comparisons between controls and disease population, we
>> need to make sure that the same fitting parameters are applied for the
>> MAPMRI fitting for avoiding any biases. Do you have suggestions regarding
>> this matter?
>>
>> Thank you.
>>
>> Ping
>>
>> On Tue, Jan 23, 2018 at 7:42 AM, Rutger Fick <fick.rutger at gmail.com>
>> wrote:
>>
>>> Hi Ping,
>>>
>>> Salt and pepper noise is not a good sign (I just didn't see it so much
>>> on the second set of slices you sent). To spot badly estimated voxels is
>>> typically pretty easy - RTOP and many others can have negative or huge
>>> values, which typically come from oscillations in the signs extrapolation.
>>> You can often see these as bright spots in the laplacian norm.
>>>
>>> If you go through the data and see that salt and pepper noise
>>> corresponds to unusually high norms, Increasing the laplacian minimum
>>> weight in the code as i told you wil usually resolve it (or fixing it to a
>>> value like 0.05 or 0.1 or something, see what works without overdoing it).
>>>
>>> Best,
>>> Rutger
>>>
>>>
>>>
>>>
>>> On 23 Jan 2018 03:06, "Ping-Hong Yeh" <pinghongyeh at gmail.com> wrote:
>>>
>>> Hi Rutger,
>>>
>>> Thank you very much for the detailed reply.
>>>
>>> I guess i do not need to worry about those salt-pepper dots?
>>>
>>> Would you recommend output laplacian norm and laplacian_weighted maps
>>> and go through the images for each data set? Any tips for realizing something
>>> really goes wrong when looking at the propagator anisotropy map?
>>>
>>> Best,
>>>
>>> Ping
>>>
>>>
>>> On Jan 22, 2018 6:55 PM, "Rutger Fick" <fick.rutger at gmail.com> wrote:
>>>
>>>> Hi Ping,
>>>>
>>>> In my experience, badly estimated voxels typically have super high
>>>> laplacian norm and very low estimated laplacian weight (lopt).
>>>> Looking at these results I would say things actually look pretty good!
>>>>
>>>> Getting the best results is always tricky finding a balance of
>>>> optimally regularizing: not fitting the noise but also not
>>>> over-regularizing, which is why the GCV option is nice.
>>>> But, in rare cases it does mess up. So, if you want to give the GCV a
>>>> bit less freedom to go low (to be on the safe side) you can increase the
>>>> minimum bound of the GCV optimization in line 2272 of the code.
>>>>
>>>> There's many ways to speed up the code I gave you if you want to put in
>>>> the effort ;-) Using parallel processing is not standardly implemented in
>>>> dipy, but maybe you can hack it somehow.
>>>> You can also set the laplacian_weight = 0.1 or something to avoid GCV,
>>>> but it won't make a huge difference. I only ever used this code to do
>>>> research - so speed was not really a concern.
>>>>
>>>> Anyway, hope this all helped! Let me know if everything works out,
>>>>
>>>> Best,
>>>> Rutger
>>>>
>>>> On 19 January 2018 at 22:03, Ping-Hong Yeh <pinghongyeh at gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Rutger,
>>>>>
>>>>> Attached please find the snapshot of norm_of_laplacian_signal, lopt,
>>>>> and pa maps of the same data set i used earlier.
>>>>>
>>>>> BTW, is there a way to speed up the mapmri_pa processing? Will the
>>>>> OpenMP help?
>>>>>
>>>>> Thank you,
>>>>>
>>>>> ping
>>>>>
>>>>> On Fri, Jan 19, 2018 at 1:25 PM, Rutger Fick <fick.rutger at gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi Ping,
>>>>>>
>>>>>> So far, so good.
>>>>>> In my opinion the TORTOISE PA reconstruction looks a bit
>>>>>> flat/overregularized - but then again I don't know what kind of
>>>>>> regularization they implemented for themselves.
>>>>>> The PA of the implementation I gave you seems to give more consistent
>>>>>> contrast for different tissue configurations - which is a good - but looks
>>>>>> like it under-regularizes in some individual voxels (the salt-pepper noise
>>>>>> in the PA/RTOP).
>>>>>>
>>>>>> To check if this is the case, can you show me the
>>>>>> mapfit_L.norm_of_laplacian_signal() and mapfit_L.lopt maps?
>>>>>>
>>>>>> Rutger
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 19 January 2018 at 17:43, Ping-Hong Yeh <pinghongyeh at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi Rutger,
>>>>>>>
>>>>>>> Just give you an update of the results (see the attached snapshots)
>>>>>>> using GCV weighted and Laplacian regularization for MAPMRI
>>>>>>> estimation.
>>>>>>>
>>>>>>> The other PA mapping was calculated using TORTOISE. I have also
>>>>>>> attached RTOP mapping calculated using DIPY with and without GCV
>>>>>>> weighted and Laplacian regularization.
>>>>>>>
>>>>>>> Comparing to the TORTOISE, PA values in the one using GCV weighted
>>>>>>> and Laplacian regularization method are relatively smaller,
>>>>>>> particularly over the regions with the less dense white matter.
>>>>>>>
>>>>>>> For RTOP images, I am not sure whether GCV weighted and Laplacian
>>>>>>> regularization method outperforms the one without using GCV
>>>>>>> weighted and Laplacian regularization.
>>>>>>>
>>>>>>> Any comments?
>>>>>>> Thank you,
>>>>>>>
>>>>>>> ping
>>>>>>>
>>>>>>> On Wed, Jan 17, 2018 at 7:48 PM, Rutger Fick <fick.rutger at gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> 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-pack
>>>>>>>>>>> ages/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-pack
>>>>>>>>>>> ages/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_perpendic
>>>>>>>>>>>>>> perpendicularular().
>>>>>>>>>>>>>> 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
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>>> Neuroimaging mailing list
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>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
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>>>>>>>>>>>>>>
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>>>>>>>>>>>>>
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