[Mne_analysis] Happy new year with Dipy 0.8!

[Eleftherios Garyfallidis]

Dear all,


We are very happy to announce a new release of Diffusion Imaging in Python
(Dipy).


Here is a summary of the most important new features and developments.


DIPY 0.8.0 (Released on Tuesday, 6 Jan 2015)


Nonlinear Image-based Registration (SyN)


An implementation of the Symmetric Normalization method for nonlinear
diffeomorphic registration. This implementation is lightweight, and does
not depend on ITK or ANTS. It is written entirely in Python and Cython.


Streamline-based Linear Registration (SLR)


A new method that allows direct registration of bundles of streamlines.
Especially useful for creating atlases of specific types of bundles.


Linear Fascicle Evaluation (LiFE)


This is a Python implementation of a new method for evaluation of
tractrography solutions.


Sparse Fascicle Model (SFM)


A new signal reconstruction method , added to the large stack of
reconstruction models already available in Dipy, including implementations
of CSD and SHORE.


Non-local means denoising (NLMEANS)


Denoising is a technique that can boost most of your analysis techniques as
it can increase the signal to noise ratio of your data. We started this new
module by implementing a very generic denoising technique that can be used
also for fMRI and T1 images.


New modular tracking machinery


This is a collection of new objects which allows rapid development of new
fiber tracking algorithms.


In summary, since January 2014 (version 0.7.1), we closed 388 issues and
merged 155 pull requests. The project now has a total of more than 4000
commits and 29 contributors.


We would appreciate if you could forward this information to any interested
individuals or labs.


Yours sincerely,


On behalf of the Dipy developers,

Eleftherios Garyfallidis

http://dipy.org
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