eprintid: 10135780
rev_number: 15
eprint_status: archive
userid: 608
dir: disk0/10/13/57/80
datestamp: 2021-10-06 13:02:06
lastmod: 2021-11-30 23:24:46
status_changed: 2021-10-06 13:02:06
type: proceedings_section
metadata_visibility: show
creators_name: Powell, E
creators_name: Battocchio, M
creators_name: Parker, CS
creators_name: Slator, PJ
title: Generalised Hierarchical Bayesian Microstructure Modelling for Diffusion MRI
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Bayesian statistics, Bayesian hierarchical model, Microstructure modelling, Diffusion MRI
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: 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.
date: 2021-09-25
date_type: published
publisher: Springer
official_url: https://doi.org/10.1007/978-3-030-87615-9_4
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1891845
doi: 10.1007/978-3-030-87615-9_4
isbn_13: 978-3-030-87614-2
lyricists_name: Parker, Christopher
lyricists_name: Slator, Patrick
lyricists_id: CSPAR73
lyricists_id: PJSLA44
actors_name: Slator, Patrick
actors_id: PJSLA44
actors_role: owner
full_text_status: public
series: Lecture Notes in Computer Science (LNCS)
publication: In: Cetin-Karayumak S. et al. (eds) Computational Diffusion MRI - CDMRI 2021
volume: 13006
place_of_pub: Cham, Switzerland
pagerange: 36-47
event_title: 12th International Workshop, CDMRI 2021
institution: CDMRI 2021
book_title: Computational Diffusion MRI: 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
editors_name: Cetin-Karayumak, S
editors_name: Christiaens, D
editors_name: Figini, M
editors_name: Guevara, P
editors_name: Gyori, N
editors_name: Nath, V
editors_name: Pieciak, T
citation:        Powell, E;    Battocchio, M;    Parker, CS;    Slator, PJ;      (2021)    Generalised Hierarchical Bayesian Microstructure Modelling for Diffusion MRI.                     In: Cetin-Karayumak, S and Christiaens, D and Figini, M and Guevara, P and Gyori, N and Nath, V and Pieciak, T, (eds.) Computational Diffusion MRI: 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings.  (pp. pp. 36-47).  Springer: Cham, Switzerland.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10135780/1/samplepaper.pdf