TY  - GEN
CY  - Cham, Switzerland
A1  - Powell, E
A1  - Battocchio, M
A1  - Parker, CS
A1  - Slator, PJ
T3  - Lecture Notes in Computer Science (LNCS)
KW  - Bayesian statistics
KW  -  Bayesian hierarchical model
KW  -  Microstructure modelling
KW  -  Diffusion MRI
N2  - 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 github.com/PaddySlator/dmipy-bayesian.
ID  - discovery10135780
PB  - Springer
UR  - https://doi.org/10.1007/978-3-030-87615-9_4
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Generalised Hierarchical Bayesian Microstructure Modelling for Diffusion MRI
SP  - 36
AV  - public
Y1  - 2021/09/25/
EP  - 47
ER  -