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

[Rutger Fick]

Hi Ping,

That's a great dataset you're using!

Estimating the MC-MDI model itself is quite fast, I think a full HCP
subject takes less than an hour.
However, the secondary parametric FOD estimation in the example (optimizing
2 bundles with slow but accurate MIX approach [1]) takes more than a second
per voxel.
You can speed this up by using more cores to parallelize the optimization
more, but it will inherently not be very fast [1].
Alternatively, you can choose to only fit one bundle and use "brute2fine"
optimization, which is much faster and probably feasible to fit to all your
volumes in a more reasonable time.

Moreover, investigating the dispersion in crossings using this 2-bundle
MC-MDI model is very interesting (and has not been done as far as I know),
but I don't recommend to fit this model to your data as a whole.
Fitting a 2-bundle model to single-bundle data is a degenerate problem
(many solutions with similar fitting error), so the dispersion parameters
outside crossing bundles won't be meaningful (see our NODDIx example
<http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_mix_microstructure_imaging_in_crossings.ipynb>
).
To still use this 2-bundle model, I suggest to make a mask where you know
there is more than 1 peak (using CSD for example), and only fit the
multi-bundle model inside these ROIs.

If you're also interested in using tractography-based comparison on your
dataset, we'll also soon release CSD-based FOD estimation for the MC-MDI
model (winning method of 2017 ISMRM tractography competition
<https://my.vanderbilt.edu/ismrmtraced2017/>).

Let me know how it goes :-)
Rutger

[1] Farooq, Hamza, et al. "Microstructure imaging of crossing (MIX) white
matter fibers from diffusion MRI." *Scientific reports* 6 (2016): 38927.


On 23 Apr 2018 17:34, "Ping-Hong Yeh" <pinghongyeh at gmail.com> wrote:

Hi Rutger,

 Thanks for the fix.

Do have the estimate of approximate time needed for doing mcdmi_fod_fit on
a data of 240*240*187 with 1mm in resolution for total 289 volumes?

It has been running for more than 2 days on a MAC OS with 2 X 2.66 GHz
6-Core, 96GB memory machine.


Thank you.

Ping

On Sun, Apr 22, 2018 at 6:10 PM, Rutger Fick <fick.rutger at gmail.com> wrote:

> Hi Ping,
>
> Great to hear you're trying the toolbox!
>
> Thanks for pointing out the bug, I just fixed it in the repository, so you
> should be able to load the gradient directions without having the error now.
>
> Dmipy is completely general in that it can import any PGSE-based
> acquisition scheme with any number of non-diffusion weighted volumes. Dmipy
> internally normalizes the signal according to the mean of all b0-values,
> and automatically detects which measurements belong to the same acquisition
> shell.
>
> Let me know if you have any more questions or just generally what your
> experience is using dmipy :-)
>
> Best,
> Rutger
>
> On 21 April 2018 at 19:54, Ping-Hong Yeh <pinghongyeh at gmail.com> wrote:
>
>> Hi Rutger,
>>
>> The failure was caused by multiple [ 0 0 0]  arrays in the gradient table
>> that were used for acquiring non-diffusion weighted volumes. It started
>> running after I modified the gradient  table by adding 1 to the z-direction
>> of [ 0 0 0] to become [ 0 0 1].
>> Can dmipy import the gradient DWI volumes with multiple non-diffusion
>> weighted volumes interspersed in-between?
>>
>> Thank you.
>>
>> Ping
>>
>>
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