Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model

Yang, Y., Gao, H. , Berry, C. , Radjenovic, A. and Husmeier, D. (2022) Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model. In: Proceedings of the 4th International Conference on Statistics: Theory and Applications (ICSTA 22), Prague, Czech Republic, 28-30 July 2022, ISBN 9781990800085 (doi: 10.11159/icsta22.146)

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Publisher's URL: https://avestia.com/ICSTA2022_Proceedings/files/paper/ICSTA_146.pdf

Abstract

Dynamic Contract Enhanced Magnetic Resonance (MR) Imaging (DCE-MRI) has been widely used as a non-invasive assessment approach to estimate the myocardial blood flow (MBF). The delineation of a hypo-perfused region (low MBF region) is important for understanding a patient’s heart condition in clinical diagnosis. In this paper, a Markov random field constrained Gaussian mixture model (GMM-MRF) classification method is introduced to classify MBF maps using myocardial perfusion DCE-MRI data. The GMM-MRF method, trained with an ICM algorithm, makes use of spatial neighbourhood information to improve classification accuracy. The proposed method is applied to and assessed on both synthetic and clinical data, and compared with established classification methods.

Item Type:Conference Proceedings
Additional Information:This work was funded by EPSRC, grant reference numbers EP/T017899/1 and EP/S020950/1. Yalei Yang is funded by a grant from GlaxoSmithKline plc.
Keywords:DCE-MRI, myocardial perfusion, classification, lesion delineation, Gaussian Mixture Model, Markov random field constrained Gaussian mixture model.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Berry, Professor Colin and Gao, Dr Hao and Yang, Dr Yalei 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 > 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
ISSN:2562-7767
ISBN:9781990800085

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
308255The SofTMech Statistical Emulation and Translation HubDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/T017899/1M&S - Statistics
303231A whole-heart model of multiscale soft tissue mechanics and fluid structureinteraction for clinical applications (Whole-Heart-FSI)Xiaoyu LuoEngineering and Physical Sciences Research Council (EPSRC)EP/S020950/1M&S - Mathematics