Quantification of Myocardial Perfusion Lesions Using Spatially Variant Finite Mixture Modelling of DCE-MRI (original) (raw)
Yang, Yalei 
ORCID: https://orcid.org/0000-0002-0353-1400, Gao, Hao ORCID: https://orcid.org/0000-0001-6852-9435, Berry, Colin ORCID: https://orcid.org/0000-0002-4547-8636, Radjenovic, Aleksandra ORCID: https://orcid.org/0000-0002-1742-6863 and Husmeier, Dirk ORCID: https://orcid.org/0000-0003-1673-7413(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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Text 193804.pdf - Published Version 759kB |
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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 |
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| 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: | Husmeier, Professor Dirk and Gao, Dr Hao and Berry, Professor Colin and Yang, Yalei 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 > School of Cardiovascular & Metabolic HealthCollege of Science and Engineering > School of Mathematics and Statistics > MathematicsCollege 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 |
| Related URLs: | Organisation |
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Funder and Project Information
1
EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancer
Raymond Ogden
EP/N014642/1
M&S - MATHEMATICS
Deposit and Record Details
| ID Code: | 193804 |
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| Depositing User: | Professor Dirk Husmeier |
| Datestamp: | 26 Aug 2019 10:06 |
| Last Modified: | 06 Apr 2025 12:56 |
| Date of acceptance: | 5 June 2019 |
| Date of first online publication: | 14 August 2019 |
| Date Deposited: | 26 August 2019 |