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
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
The notebook of the practical can be found here: bm_dwi_practical.ipynb
- nibabel
- dipy
- nilearn
- jupyter
For configuration on Telecom ParisTech computers, you can follow the steps in python_setup.sh.
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.