Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics

Romaszko, L., Borowska, A. , Lazarus, A., Dalton, D., Berry, C. , Luo, X. , Husmeier, D. and Gao, H. (2021) Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artificial Intelligence In Medicine, 119, 102140. (doi: 10.1016/j.artmed.2021.102140)

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Abstract

Combining biomechanical modelling of left ventricular (LV) function and dysfunction with cardiac magnetic resonance (CMR) imaging has the potential to improve the prognosis of patient-specific cardiovascular disease risks. Biomechanical studies of LV function in three dimensions usually rely on a computerized representation of the LV geometry based on finite element discretization, which is essential for numerically simulating in vivo cardiac dynamics. Detailed knowledge of the LV geometry is also relevant for various other clinical applications, such as assessing the LV cavity volume and wall thickness. Accurately and automatically reconstructing personalized LV geometries from conventional CMR images with minimal manual intervention is still a challenging task, which is a pre-requisite for any subsequent automated biomechanical analysis. We propose a deep learning-based automatic pipeline for predicting the three-dimensional LV geometry directly from routinely-available CMR cine images, without the need to manually annotate the ventricular wall. Our framework takes advantage of a low-dimensional representation of the high-dimensional LV geometry based on principal component analysis. We analyze how the inference of myocardial passive stiffness is affected by using our automatically generated LV geometries instead of manually generated ones. These insights will inform the development of statistical emulators of LV dynamics to avoid computationally expensive biomechanical simulations. Our proposed framework enables accurate LV geometry reconstruction, outperforming previous approaches by delivering a reconstruction error 50% lower than reported in the literature. We further demonstrate that for a nonlinear cardiac mechanics model, using our reconstructed LV geometries instead of manually extracted ones only moderately affects the inference of passive myocardial stiffness described by an anisotropic hyperelastic constitutive law. The developed methodological framework has the potential to make an important step towards personalized medicine by eliminating the need for time consuming and costly manual operations. In addition, our method automatically maps the CMR scan into a low-dimensional representation of the LV geometry, which constitutes an important stepping stone towards the development of an LV geometry-heterogeneous emulator.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Berry, Professor Colin and Dalton, David and Gao, Dr Hao and Romaszko, Mr Lukasz and Lazarus, Mr Alan and Luo, Professor Xiaoyu and Borowska, Dr Agnieszka and Husmeier, Professor Dirk
Authors: Romaszko, L., Borowska, A., Lazarus, A., Dalton, D., Berry, C., Luo, X., Husmeier, D., and Gao, H.
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
Journal Name:Artificial Intelligence In Medicine
Publisher:Elsevier
ISSN:0933-3657
ISSN (Online):1873-2860
Published Online:11 August 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Artificial Intelligence In Medicine 119: 102140
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
303232EPSRC Centre for Multiscale soft tissue mechanics with MIT and POLIMI (SofTMech-MP)Xiaoyu LuoEngineering and Physical Sciences Research Council (EPSRC)EP/S030875/1M&S - Mathematics
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
172141EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancerRaymond OgdenEngineering and Physical Sciences Research Council (EPSRC)EP/N014642/1M&S - Mathematics
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science
308255The SofTMech Statistical Emulation and Translation HubDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/T017899/1M&S - Statistics
305655Inference of cardio-mechanical parameters in real time: moving mathematical modelling into the clinicDirk HusmeierThe Royal Society of Edinburgh (ROYSOCED)62335M&S - Statistics
303944BHF Centre of ExcellenceRhian TouyzBritish Heart Foundation (BHF)RE/18/6/34217CAMS - Cardiovascular Science