Title: Sharing information across voxels with Bayesian hierarchical modelling to improve brain microstructure mapping
Project lead: Paddy Slator (email: firstname.lastname@example.org, mattermost: paddyslator)
Project collaborators: Chris Parker Lizzie Powell Matteo Battocchio
Registered Brainhack Global 2020 Event: Brainhack Atlantis The Atlantic Ocean - Micro2Macro
AIM: Implement a hierarchical Bayesian fitting procedure for a range of brain microstructural models.
Typically microstructural models are fitted “voxel-by-voxel” to diffusion MRI (dMRI) data, with the implicit assumption that each image voxel is an independent measurement. Some recent techniques break this assumption, exploiting data redundancy to improve model fits and subsequent mappings.
One such method is the Bayesian hierarchical intravoxel incoherent motion model (IVIM) introduced by Orton et al. (https://doi.org/10.1002/mrm.24649). Here the posterior distribution encodes voxelwise microstructural parameter estimates, and the prior distribution encodes parameter means and covariance across a larger ROI. By applying Bayes’ Rule and inferring the model with a Markov chain Monte Carlo (MCMC) algorithm, they improve IVIM parameter mappings of liver dMRI compared to standard methods.
This project will adapt this approach to brain microstructure modelling.
Data to use:
We will test the method on a (to be chosen later) Human connectome project (HCP) dMRI scan (https://www.humanconnectome.org/study/hcp-young-adult/data-releases).
Link to project repository/sources:
https://github.com/PaddySlator/dmipy This is a fork of the dmipy (Diffusion Microstructure Imaging in Python) repository. The project will utilise and adapt this code, with the ultimate goal of integrating the developed tools with dmipy.
Goals for Brainhack Global 2020:
Deliverable 1: Implement MCMC algorithm for Bayesian hierarchical brain microstructure modelling Deliverable 2: Test MCMC algorithm on an HCP dMRI scan
Good first issues:
Tools/Software/Methods to Use:
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