slimnsour/intro_to_dmri_workshop

Intro_to_dMRI_workshop

This repo integrate a previous repo created as part of Neuroimaging Carpentry and includes tutorial for working with dMRI data using DIPY.

About the Lesson

This lesson teaches:

• What diffusion Magnetic Resonance Imaging is
• How dMRI data is organized within the BIDS framework
• What the standard preprocessing steps in dMRI are
• How local fiber orientation can be reconstructed using dMRI data
• How dMRI can provide insight into structural white matter connectivity

Local

If users choose to run the Jupyter notebooks locally, the following dependencies will need to be installed:

• [ANTs] : used to register different anatomical data.
• [FSL] : used for different data preprocessing steps.
• [DIPY] : used for diffusion MRI data processing.
• [FURY] : used for anatomical data visualisation purposes.
• [Matplotlib] : used for data visualisation purposes.
• [Nilearn] : used for anatomical data visualisation purposes.
• [osfclient] : used to download the necessary data.
• [PyBIDS] : used to check the data structure [BIDS] compliance.

To run the code locally please follow the instructions:

1. Clone this repo using:

git clone https://github.com/Davi1990/Intro_to_dMRI_workshop

2. Install the required dependencies using:

pip install -r requirements.txt

3. Download the necessary data

Notebooks expect them to be placed in the data folder that exists in the root of the repository.

Note that the above command clones the entire repository, which may be quite large and take a while to download. Alternatively, data from a single subject is available and can be downloaded by running:

$ cd ./data
$ osf -p cmq8a fetch ds000221_subject/ds000221_sub-010006.zip
$ unzip ds000221_sub-010006.zip

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Extra steps

Test the installation

Test installation information for a package can be checked by running, for example:

$ pip show dipy

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Similarly, it can be checked that a given package can be imported in Python by running, for example:

$ python
>>> import dipy

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Alternatively, the package version can also be checked by running, for example:

$ python
>>> import dipy
>>> print(dipy.__version__)

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You can also see the packages and versions of all pip-installed dependencies by typing:

$ pip freeze

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In order to run the notebooks, the notebook server needs to be started. Once the current directory changed to the root of the code directory, the server is started running:

$ ipython notebook

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if using IPython, and running:

jupyter-lab

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if using JupyterLab.

In either case, the commands will print some information about the notebook server in the terminal, and a web browser will be opened to the URL of the web application (by default, http://127.0.0.1:8888). The users will be presented to the directory structure of the current directory, and they will be able to run the notebook of interest.

For additional information about Python setups besides the package manuals, users are encouraged to read the [Programming with Python] Carpentries lesson.

The data used in the lesson is hosted in [OSF]. It can be downloaded by running:

$ osf -p cmq8a clone ./data

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