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dipy.org/pull/52/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: e3883b7708c3613e644f3a9c9d7b017c
+tags: 645f666f9bcd5a90fca523b33c5a78b7

dipy.org/pull/52/CNAME

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+dipy.org

dipy.org/pull/52/_sources/blog.rst.txt

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+====
+Blog
+====

dipy.org/pull/52/_sources/calendar.rst.txt

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+.. _calendar:
+
+========
+Calendar
+========
+
+You can stay updated with upcoming DIPY_ events. Checkout our events calendar.
+
+.. raw:: html
+
+    <iframe class="calendar" src="https://calendar.google.com/calendar/embed?src=uv8c50fkfvs529837k298ueqh0%40group.calendar.google.com&ctz=America%2FIndiana%2FIndianapolis" title="DIPY Calendar"></iframe>
+
+
+Get Calendar
+--------------
+You can also add DIPY_ calendar to your google calendar with this `link. <https://calendar.google.com/calendar/u/0?cid=dXY4YzUwZmtmdnM1Mjk4MzdrMjk4dWVxaDBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ>`_
+
+.. include:: links_names.inc
+

dipy.org/pull/52/_sources/index.rst.txt

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+.. toctree::
+   :maxdepth: 2
+   :hidden:
+
+   blog
+   calendar

dipy.org/pull/52/_sources/posts/2015/2015_gsoc_announcement.rst.txt

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+Google Summer of Code 2015 Announcement
+=======================================
+
+.. post:: January 15 2015
+     :author: Eleftherios Garyfallidis
+     :tags: google
+     :category: gsoc announcement
+
+We are happy to announce our application for the Google Summer of Code 2015. 
+
+If you are interested in participating as a student, please read `this <https://wiki.python.org/moin/SummerOfCode/Expectations>`_ first.
+
+GSoC is a project that enables students to learn by contributing to an open-source project, while receiving a stipend from Google, and mentorship from open-source software developers. For details about this year's GSoC, please refer to `this page <http://www.google-melange.com/gsoc/homepage/google/gsoc2015>`_.
+
+All participants should have a basic knowledge of scientific programming in Python.
+Recommended book `Python for Data Analysis by Wes McKinney <http://shop.oreilly.com/product/0636920023784.do>`_. 
+
+Here are the projects we offer to mentor this summer:
+
+1. **3D visualizations**
+
+   Description: The main tool for 3D visualization in dipy is the dipy.viz.fvtk module. 
+   This creates `beautiful images <https://www.youtube.com/watch?v=kstL7KKqu94>`_, but the functionality is currently limited, and we would like to expand it. This project will create a more generic API that allows visualization of peaks, ODFs, volumes and streamlines in the correct space. Also implement VTK's network visualization in fvtk. Get creative! Many other things can be done here! For example enabling recording of 3D animations of the brain, create glass effects etc. 
+
+   Difficulty: high. 
+
+   Skills required: acquaintance with VTK is an advantage, knowledge of 3D graphics is required. 
+
+   Mentors: `Eleftherios Garyfallidis <mailto:[email protected]>`_ and `Ariel Rokem <mailto:[email protected]>`_ and `Matthew Brett <mailto:[email protected]>`_.
+
+2. **Use directional information to improve dMRI registration**
+
+   Description: Currently in DIPY we have a framework for nonlinear registration based on the idea of Symmetric Normalization `SyN <http://www.ncbi.nlm.nih.gov/pubmed/17659998>`_. This framework allows to create new similarity metrics (e.g. cross correlation, or mutual information) and let the optimization of SyN to warp the images. Now, in diffusion MRI we can have in each voxel orientation distributions. The goal of the project is to use additionally this orientation information to drive the registration. So now we do not only warp but also re-orient the orientation distributions while warping. In other words, you will have to create a new orientation distribution based metric which will work inside our existing SyN framework. `This paper <http://www.ncbi.nlm.nih.gov/pubmed/21316463>`_ is a must read.
+
+   Difficulty: high 
+
+   Skills required: expertise in registration; acquaintance with diffusion modelling. 
+
+   Mentors: `Matthew Brett <mailto:[email protected]>`_ and `Eleftherios Garyfallidis <mailto:[email protected]>`_ and `Matthew Brett <mailto:[email protected]>`_.  
+
+3. **Diffusion Kurtosis Imaging**
+
+   Description: `Diffusion Kurtosis Imaging <http://www.ncbi.nlm.nih.gov/pubmed/20632416>`_, or DKI, is a method that estimates the parameters of higher-order statistics in DWI data with multiple b-value measurements (such as measurements from the `Human Connectome Project <http://www.humanconnectome.org/>`_. This allows us to make inferences about properties of the tissue that are not readily available with other methods, such as DTI. We have already `begun <https://github.com/nipy/dipy/pull/233>`_ the work on an implementation of this algorithm, but the work needs to be completed, and there is still much to do here.
+
+   Difficulty: high. 
+
+   Skills required: acquaintance with diffusion MRI. 
+
+   Mentors: `Ariel Rokem <mailto:[email protected]>`_ and `Eleftherios Garyfallidis <mailto:[email protected]>`_ and `Matthew Brett <mailto:[email protected]>`_.
+
+4. **Offline quality assurance (QA) using a publicly available dataset**
+
+   Description: The ultimate demonstration of a tool is in its use in realistic and important cases. The analysis of high-quality publicly available data-sets (e.g. from the `Human Connectome Project <http://www.humanconnectome.org/>`_) is one compelling case. The goal of this project, is to create a pipeline for analysis of such a data-set, and to reproducibly execute this analysis as a way to benchmark the tools available through dipy, and perform QA, to detect regressions in the performance of these tools. This will also be a public show-case of the project, as a way to interest new users. 
+
+   Difficulty: intermediate. 
+
+   Skills required: acquaintance with diffusion MRI, and with dipy. 
+
+   Mentors: `Ariel Rokem <mailto:[email protected]>`_ and `Eleftherios Garyfallidis <mailto:[email protected]>`_ and `Matthew Brett <mailto:[email protected]>`_.
+
+5. **Tissue classifiers for tracking**
+
+   Description: Research in the last couple of years has shown that using a tissue classifier in tracking can be of great benefit for creating more accurate representations of the underlying white matter anatomy. The goal of this project will be to create accurate tissue classifiers to guide tracking. So this is basically an image segmentation task. To do that, we will have to implement a couple of popular algorithms using T1-weighted and/or invent a new one using DWI data. Sounds fun?
+
+   Difficulty: intermediate. 
+
+   Skills required: acquaintance with diffusion MRI and image segmentation. 
+
+   Mentors: `Eleftherios Garyfallidis <mailto:[email protected]>`_ and `Ariel Rokem <mailto:[email protected]>`_ and `Matthew Brett <mailto:[email protected]>`_

dipy.org/pull/52/_sources/posts/2016/2016_gsoc_announcement.rst.txt

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+How to become a part of DIPY's Google Summer of Code 2016
+=========================================================
+
+.. post:: February 01 2016
+     :author: Eleftherios Garyfallidis
+     :tags: google
+     :category: gsoc announcement
+
+GSoC is a program that allows students to learn by contributing to an open-source project, while receiving a fellowship from Google, and mentorship from open-source software developers. For details about this year's GSoC, please refer to `this page <https://developers.google.com/open-source/gsoc/>`_.
+
+Before considering becoming part of the Dipy GSoC, please read about our `expectations <https://wiki.python.org/moin/SummerOfCode/Expectations>`_. 
+
+All participants should have basic knowledge of scientific computing and development in Python. For a comprehensive introduction to these topics, please refer to the book `Effective Computation in Physics <http://shop.oreilly.com/product/0636920033424.do>`_ by Katy Huff and Anthony Scopatz. 
+
+Projects
+--------
+
+1. **Continuous quality assurance (QA) in cloud computing environment**
+
+   Description: The ultimate demonstration of a tool is in its use in realistic and important cases. The analysis of high-quality publicly available data-sets (e.g. from the `Human Connectome Project <http://www.humanconnectome.org/>`_) is one compelling case. The goal of this project, is to create a pipeline for analysis of such a data-set, and to reproducibly execute this analysis on a cloud computing resource, as a way to benchmark the tools available through dipy, and perform QA, to detect regressions in the performance of these tools. This will also be a public show-case of the project, as a way to interest new users. 
+
+   Difficulty: intermediate. 
+
+   Skills required: acquaintance with diffusion MRI, and with dipy. Acquaintance with cloud computing is a plus.
+
+   Mentors: `Ariel Rokem <mailto:[email protected]>`_ and `Eleftherios Garyfallidis <mailto:[email protected]>`_.
+
+2. **CHARMED: biophysical modeling of multi b-value data**
+
+   Description: The `CHARMED model <http://www.ncbi.nlm.nih.gov/pubmed/15979342>`_ describes the diffusion signal as a combination of hindered and restricted components. This advanced model, when applied to data with multiple b-values, can be used to make inferences about tissue structure and biophysics. The GSoC project will focus on an efficient and well-tested implementation of the CHARMED model in the Dipy reconstruction module. 
+
+   Difficulty: intermediate
+
+   Mentors: `Ariel Rokem <mailto:[email protected]>`_ and Rafael Henriques.
+
+3. **Develop a new DIPY website with more interactive features (project is full)**
+
+   Description: The current `DIPY <http://dipy.org>`_ website is based on Sphinx and allows for only one documentation to be online (the development version). One of the tasks of this project will be to create a new github repository which will be only for Dipy's website. Right now the website is under the doc folder of the dipy repository. In this new repository a new responsive website will be created which upon other things will allow for hosting documentations for multiple versions. Additionally, the new website will allow for direct insertion of news and connections and updates to social media. Most importantly, new algorithms are expected to be developed that will increase UX. More details soon.
+
+   Difficulty: intermediate
+
+   Skills required: Django, bootstrap, javascript, sphinx and expertise in web development
+
+   Project is full: We had already more than 40 excellent people applying for this project and it will be impossible to interview more of them. **So, this specific project is now closed for new applicants, contacting us after 2nd of March**. Please look and apply to the other exciting projects. 
+
+   Mentors: `Jean-Christophe Houde <mailto:[email protected]>`_ and `Eleftherios Garyfallidis <mailto:[email protected]>`_
+
+4. **DKI enhancements**
+
+   Description: diffusion kurtosis imaging (DKI) is an extension of the classic DTI model. In the previous GSoC, `Rafael Henriques implemented the DKI model fitting and estimation <http://gsoc2015dipydki.blogspot.com/>`_. This project proposes to extend our current implementation of diffusion kurtosis with a few different improvements. The first extension will allow us to estimate additional parameters of white matter "integrity" based on the diffusion kurtosis model (see `Fieremans et al. paper <http://www.sciencedirect.com/science/article/pii/S1053811911006148>`_). The second extension will allow us to use the DKI model for tractography (see `tractography paper <http://www.ncbi.nlm.nih.gov/pubmed/26275886>`_). Finally, we will also implement the REKINDLE algorithm, which allows robust fitting of DKI parameters (see `REKINDLE paper <http://onlinelibrary.wiley.com/doi/10.1002/mrm.25165/abstract>`_).
+
+   Difficulty: high -- knowledge in diffusion MRI preferred
+
+   Mentors: `Ariel Rokem <mailto:[email protected]>`_ and Rafael Henriques
+
+5. **IVIM: Simultaneous modeling of perfusion and diffusion**
+
+   Description: The IVIM model uniquely describes the diffusion and perfusion from data with multiple b-values (see `Le Bihan et al. paper <http://pubs.rsna.org/doi/abs/10.1148/radiology.161.2.3763909>`_ or `Luciani et al. paper <http://onlinelibrary.wiley.com/doi/10.1002/jmri.24195/full>`_). It has been used to investigate brain disease, stroke, aging, and liver fibrosis among other medical and neuroscience applications. This project proposes porting a `previous implementation of IVIM processing by Eric Peterson <https://github.com/etpeterson/IVIM_fitting>`_ into Dipy. Further extensions would be to implement the Jacobian for speed improvements in the nonlinear fitting and improvements in the fitting algorithm to improve robustness.
+
+   Difficulty: intermediate
+
+   Mentors: `Ariel Rokem <mailto:[email protected]>`_, Eric Peterson and `Rafael Henriques <mailto:[email protected]>`_.
+
+6. **Scifi UI using Python-VTK in DIPY.VIZ**
+
+   Description: The main idea will be to develop new futuristic widgets directly using VTK (Visualization Toolkit) without calling any external libraries. So, no Qt! Only VTK which is written already in OpenGL. Here are some recent tutorials to have a look http://dipy.org/examples_index.html#id15 and start playing with.
+   Those new widgets are useful because we want to use them to navigate in tractographies and allow neurosurgeons and other neuroscientists to have a unique impression and user experience when using our tools. We also want to be lightweight and as multiplaform as possible.
+   Have you watched Guardians of the Galaxy? We want to create with this project the very basic tools so that in some years, we can do something like that https://vimeo.com/103533906 but of course applied for tractography exploration not for space travelling. For example, some of the tasks will be to develop a filedialogue, a sliding panel with buttons and add dynamic actor menus (3D menus on objects - like in games). 
+
+   Difficulty: high
+
+   Skills required: Python, OpenGL and VTK
+
+   Mentors: `Marc-Alexandre Côté <mailto:[email protected]>`_ and `Eleftherios Garyfallidis <mailto:[email protected]>`_
+
+7. **Automatic denoising and robust brain extraction**
+
+   Description: Create a method for automatic denoising of diffusion MRI and structural MR datasets. Currently we need to estimate the noise of the signal which is often a bit troublesome. Local PCA will be the main method to try to implement in this project but the harder task will be to do so efficiently in Python/Cython without extra dependencies. After implementing this method, the next task will be to create a more robust brain extraction method from what is currently implemented in DIPY. For this task the student will have to think of his own strategies and take decisions on which methods to combine or implement to do so. 
+
+   Difficulty: high
+
+   Skills required: Python, Numpy, diffusion MRI, signal processing, DIPY
+
+   Mentors: `Eleftherios Garyfallidis <mailto:[email protected]>`_, Omar Ocegueda, `Julio Villalon <mailto:[email protected]>`_ and `Rafael Henriques <mailto:[email protected]>`_.
+
+8. **Eddy current correction**
+
+   Description: Eddy currents are artifacts that affect diffusion MRI measurements. A common preprocessing step is to correct for these artifacts. In this project, we will implement a `popular algorithm for eddy current correction <http://www.ncbi.nlm.nih.gov/pubmed/14705050>`_.
+
+   Difficulty: moderate
+
+   Skills required: Familiarity with diffusion MRI, numpy, scipy.
+
+   Mentors: `Ariel Rokem <mailto:[email protected]>`_ and Bob Dougherty.