Classification of myocardial blood flow based on dynamic contrast-enhanced magnetic resonance imaging using hierarchical Bayesian models

Yang, Y. , Gao, H. , Berry, C. , Carrick, D., Radjenovic, A. and Husmeier, D. (2022) Classification of myocardial blood flow based on dynamic contrast-enhanced magnetic resonance imaging using hierarchical Bayesian models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(5), pp. 1085-1115. (doi: 10.1111/rssc.12568)

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising approach to assess microvascular blood flow (perfusion) within the myocardium, and the Fermi microvascular perfusion model is widely applied to extract estimates of the myocardial blood flow (MBF) from DCE-MRI data sets. The classification of myocardial tissues into normal (healthy) and hypoperfused (lesion) regions provides new opportunities for the diagnosis of coronary heart disease and for advancing our understanding of the aetiology of this highly prevalent disease. In the present paper, the Fermi model is combined with a hierarchical Bayesian model (HBM) and a Markov random fields prior to automate this classification. The proposed model exploits spatial context information to smooth the MBF estimates while sharpening the edges between lesions and healthy tissues. The model parameters are approximately sampled from the posterior distribution with Markov chain Monte Carlo (MCMC), and we demonstrate that this enables robust classification of myocardial tissue elements based on estimated MBF, along with sound uncertainty quantification. A well-established traditional method, based on a Gaussian mixture model (GMM) trained with the expectation–maximisation algorithm, is used as a benchmark for comparison.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Berry, Professor Colin and Yang, Yalei and Gao, Dr Hao and Husmeier, Professor Dirk and Carrick, Dr David and Radjenovic, Dr Aleksandra
• Authors: Yang, Y., Gao, H., Berry, C., Carrick, D., Radjenovic, A., and Husmeier, D.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health; College of Science and Engineering > School of Mathematics and Statistics > Mathematics; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Journal of the Royal Statistical Society: Series C (Applied Statistics)
• Publisher: Wiley
• ISSN: 0035-9254
• ISSN (Online): 1467-9876
• Published Online: 12 May 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 71(5): 1085-1115
• Publisher Policy: Reproduced under a Creative Commons License

Related URLs

• Research Data