Introduction to Diffusion Magnetic Resonance Imaging

Lecture and practical: Tractography, From Diffusion-weighted MRI to brain anatomical connectivity.

UE 3.11b – Advanced NeuroImaging Data Modeling and Analysis of Master BioMedical Engineering, Track BioImaging (BIM)

Pauline Roca

Research scientist in the team: Imaging Biomarkers for brain development and disorders, Centre de Psychiatrie et Neurosciences, INSERM U894 Centre Hospitalier Sainte-Anne, Paris, FR

Objectives

This practical is an introduction to diffusion Magnetic Resonance Imaging.

You will manipulate diffusion MRI data using python and:

• familiarize yourself with common neuroimaging modules: nibabel, dipy and nilearn
• better understand the MR signal in diffusion MRI (by plotting diffusion MR signal values in different tissues)
• apply classic local models to model the diffusion signal (diffusion tensor model and spherical model) and compare them
• do a whole brain tractography using dipy

There are also BONUS exercices about :

• Correction for susceptibility-induced spatial distortions
• Brain segmentation and diffusion weighted imaging
• Local modeling using FSL
• Tractography using FSL

Notebook:

The notebook of the practical can be found here: bm_dwi_practical.ipynb

Requirements

• nibabel
• dipy
• nilearn
• jupyter

Installation

For configuration on Telecom ParisTech computers, you can follow the steps in python_setup.sh.

Data

We will use data from FSL courses on diffusion MRI, some dipy datasets and some Sainte-Anne Hospital data.

The link towards the different datasets are in the notebook.