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Generalised hierarchical bayesian microstructure modelling for diffusion MRI

Powell, Elizabeth, Battocchio, Matteo, Parker, Christopher S. and Slator, Paddy J. ORCID: 2021. Generalised hierarchical bayesian microstructure modelling for diffusion MRI. Presented at: International Workshop on Computational Diffusion MRI, 01 October 2021. Published in: Cetin-Karayumak, S. ed. Computational Diffusion MRI. CDMRI 2021. Lecture Notes in Computer Science , vol.13006 Springer Nature, 36–47. 10.1007/978-3-030-87615-9_4

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Microstructure imaging combines tailored diffusion MRI acquisition protocols with a mathematical model to give insights into subvoxel tissue features. The model is typically fit voxel-by-voxel to the MRI image with least squares minimisation to give voxelwise maps of parameters relating to microstructural features, such as diffusivities and tissue compartment fractions. However, this fitting approach is susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Data-driven Bayesian hierarchical modelling defines prior distributions on parameters and learns them from the data, and can hence reduce such noise effects. Bayesian hierarchical modelling has been demonstrated for microstructure imaging with diffusion MRI, but only for a few, relatively simple, models. In this paper, we generalise hierarchical Bayesian modelling to a wide range of multi-compartment microstructural models, and fit the models with a Markov chain Monte Carlo (MCMC) algorithm. We implement our method by utilising Dmipy, a microstructure modelling software package for diffusion MRI data. Our code is available at

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Nature
ISBN: 978-3-030-87614-2
Date of First Compliant Deposit: 13 September 2023
Date of Acceptance: 2021
Last Modified: 07 Nov 2023 14:31

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