[Neuroimaging] [Nipy-devel][Dipy] A new circle of Google Summer of Code starts - time for new proposals

[Ariel Rokem]

Hi Julio,

Would be great to have a hand. Especially in reviewing code from new
contributors. That's always our bottle-neck...

On Tue, Feb 2, 2016 at 4:59 PM, Julio Villalon <jevillalonr at gmail.com>
wrote:

> Hi Eleftherios + Nipy community,
>
> This is really great. GSoC 2015 was a very rewarding experience for me. I
> am willing to be a co-mentor with Eleftherios, Ariel and Omar this year.
>
> I have some ideas which I would like to share with you.
>
> 1. Bias field/non-uniformity correction of T1-weighted images. There are
> many freely available tools that do this: SPM, FSL-FAST, N3 (MINC and
> Freesurfer), N4 (ANTS), BrainVoyager, etc. The idea is to implement the
> best one of these and include it as part of the processing pipeline of
> Dipy. Bias Field correction of T1 images allows for better segmentation and
> consequently for better partial volume estimation of brain tissue types,
> which ultimately has a direct impact on novel tractography techniques such
> as Anatomically-Constrained Tractography (ACT).
>
> - Does anyone know is there is anything available in Python?
> - Which of the mentioned methods is better?
>
>
I don't know the answer to either of these questions, but I think this is
in principle a good idea, as long as it's well scoped. Do you want to take
a shot at writing this up on the wiki ideas page?


>
> 2. Recovery of local intra-voxel fiber structure for DTI-like
> sampling/acquisition schemes (6-40 samples, b<=1200 s/mm2). Most of the
> acquired Diffusion MRI (DMRI) data available nowadays is data acquired for
> clinical/neuroscientific  studies looking at the effects of many diseases
> on the brain (schizophrenia, bipolar disorder, HIV, autism, Alzheimer's
> Disease, etc). The vast majority of this data has been acquired with
> sampling schemes with less than 40 samples and a single shell of less than
> 1200 s/mm2. With recent advancements in dictionary learning and sparse
> recovery techniques (e.g. Merlet et al, 2013), the idea is to make these
> tools available to the general public, especially to those who have this
> type of data and can make a better use of it.
>

Arguably, we already have a method like this implemented in dipy in the
Sparse Fascicle Model. See Figure 10 of our paper (
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123272),
that shows the convergence in accuracy of the model as a function of
directions. Arguably it reaches fairly high accuracy already at 40
directions, and particularly for low b values. But having more methods
would be great, of course. Could you please provide a link to the Merlet
paper you mentioned?

Cheers,

Ariel


>
>
Please let me know what you think.
>
> Thanks
>
> Julio
>
>
> 2016-01-31 13:35 GMT-08:00 Eleftherios Garyfallidis <
> garyfallidis at gmail.com>:
>
>> Hello all,
>>
>> Taking part in Google Summer of Code (GSoC) is indeed rewording for our
>> project as it allows for new algorithms to be merged and at the same time
>> grow our development team with excellent contributors.
>>
>> After I believe a successful GSoC participation last year a new circle
>> starts for this year (2016).
>>
>> Last year it was Ariel and me who did most the mentoring. This year we
>> would like to hear others' ideas too. Therefore, we welcome other
>> developers/scientists who would like to mentor or propose new projects. For
>> those who want to mentor we will be happily co-mentors to help them with
>> the process and give extra feedback to the relevant students.
>>
>> In the following link we started adding projects that we think would be
>> interesting for this year's GSoC
>>
>> https://github.com/nipy/dipy/wiki/Google-Summer-of-Code-2016
>>
>> Be happy to add your projects at the wiki or suggest ideas in this
>> thread. We also welcome the previous participants of GSoC (Julio and
>> Rafael) to take part as mentors this year.
>>
>> Finally, this year I am hoping to be able to get more than 2 projects
>> funded. Hopefully 4 but that is not certain.
>>
>> Waiting for your ideas/suggestions. What would you like to see in Dipy
>> that could be developed by a student during this summer?
>>
>> Best regards,
>> Eleftherios
>>
>>
>>
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>>
>
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