Quantification of Myocardial Perfusion Lesions Using Spatially Variant Finite Mixture Modelling of DCE-MRI

Yang, Y., Gao, H. , Berry, C. , Radjenovic, A. and Husmeier, D. (2019) Quantification of Myocardial Perfusion Lesions Using Spatially Variant Finite Mixture Modelling of DCE-MRI. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 26. ISBN 9781927877647 (doi:10.11159/icsta19.26)

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Abstract

Dynamic Contract Enhanced Magnetic Resonance (MR) Imaging (DCE-MRI) can reveal differences in myocardial perfusion (microvascular or capillary blood flow) within the myocardium. The detection and quantification of hypo-perfused lesions within the myocardium is important for understanding aetiology of coronary heart disease (CHD). In this paper, a modification of a traditional method, the Expectation-Maximization (EM) algorithm for Gaussian Mixture Models (GMM), is implemented. This modification, the Spatially Variant Finite Mixture Model (SVFMM), is able to take the neighbourhood information of a voxel in the MR image into account. An experiment based on both synthetic and real images illustrates and quantifies the improvement achieved with SVFMM over the traditional GMM method.

Item Type:Conference Proceedings
Additional Information:This work was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), grant number EP/N014642/1. Yalei Yang is funded by a grant from GlaxoSmithKline plc. Dirk Husmeier is supported by a grant from the Royal Society of Edinburgh, award number 62335. Colin Berry was supported by grants from the British Heart Foundation (PG/11/228474; RE/18/6134217).
Keywords:DCE-MRI, myocardial perfusion, lesion quantification, Gaussian Mixture Model, Spatially Variant Finite Mixture Model.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Berry, Professor Colin and Yang, Yalei and Gao, Dr Hao and Husmeier, Professor Dirk and Radjenovic, Dr Aleksandra
Authors: Yang, Y., Gao, H., Berry, C., Radjenovic, A., and Husmeier, D.
Subjects:Q Science > QA Mathematics
College/School:College of Medical Veterinary and Life Sciences > Institute of Cardiovascular and Medical Sciences
College of Science and Engineering > School of Mathematics and Statistics > Mathematics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
ISSN:2562-7767
ISBN:9781927877647
Copyright Holders:Copyright © 2019 International ASET Inc.
First Published:First published in Proceedings Proceedings of the International Conference on Statistics: Theory and Applications (ICSTA’19): 26
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
694461EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancerRaymond OgdenEngineering and Physical Sciences Research Council (EPSRC)EP/N014642/1M&S - MATHEMATICS