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An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number716597
JournalFrontiers in Physiology
Volume12
DOIs
Published16 Sep 2021

Bibliographical note

Funding Information: DN would like to acknowledge funding from Engineering and Physical Sciences Research Council (EP/N011554/1 and EP/R003866/1). This work was also supported by the Wellcome ESPRC Centre for Medical Engineering at King’s College London (WT 203148/Z/16/Z) and the British Heart Foundation (TG/17/3/33406). AY would like to acknowledge funding from the National Heart, Lung and Blood Institute (NIH R01HL121754). Publisher Copyright: © Copyright © 2021 Miller, Kerfoot, Mauger, Ismail, Young and Nordsletten.

King's Authors

Abstract

Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.

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