Assessment of Passive Left-Ventricular Myocardial Stiffness in Healthy Subjects and in Patients with Non-ischemic Dilated Cardiomyopathy. Annals

Content Provider	Semantic Scholar
Author	Asner, Liya Chabiniok, Radomír Sammut, Eva Wong, Jennifer Peressutti, Devis Kerfoot, Eric King, Andrew. Lee, Jack Razavi, Reza Smith, Nicolas Carr, Gerald D. White Nordsletten, David A.
Copyright Year	2016
Abstract	Patient-specific modelling has emerged as a tool for studying heart function, demonstrating the potential to provide non-invasive estimates of tissue passive stiffness. However, reliable use of model-derived stiffness requires sufficient model accuracy and unique estimation of model parameters. In this paper we present personalised models of cardiac mechanics, focusing on improving model accuracy, while ensuring unique parametrisation. The influence of principal model uncertainties on accuracy and parameter identifiability was systematically assessed in a group of patients with dilated cardiomyopathy (n 1⁄4 3) and healthy volunteers (n 1⁄4 5). For all cases, we examined three circumferentially symmetric fibre distributions and two epicardial boundary conditions. Our results demonstrated the ability of data-derived boundary conditions to improve model accuracy and highlighted the influence of the assumed fibre distribution on both model fidelity and stiffness estimates. The model personalisation pipeline—based strictly on noninvasive data—produced unique parameter estimates and satisfactory model errors for all cases, supporting the selected model assumptions. The thorough analysis performed enabled the comparison of passive parameters between volunteers and dilated cardiomyopathy patients, illustrating elevated stiffness in diseased hearts. Keywords—Stiffness, Myocardium, Patient-specific modelling, Model uncertainties, Parameter uniqueness. INTRODUCTION With cardiovascular disease being the leading cause of death worldwide, significant research effort has been devoted to understanding heart function in health and pathology. As a wide range of aetiologies have been attributed to cardiac conditions, determining the factors influencing disease in individual patients—and selecting appropriate treatments—remains an ongoing challenge. In some cases, such as hypertrophic cardiomyopathy, myocardial infarction and diastolic heart failure, abnormalities in tissue stiffness have been identified as features of the disease. Structural alterations associated with the severity of the condition appear to be reflected in myocardial stiffness, suggesting its potential clinical utility in improving patient assessment and providing tailored treatment strategies. Quantification of myocardial stiffness is not a straightforward task. Shear and stretch tests have been performed on animal and human tissue samples to provide a basis for estimation of myocardial properties. While the utility of these studies can hardly be overstated, the numerical values obtained from ex vivo data cannot necessarily be directly applicable in personalised in vivo studies. Alternatively, a number of techniques have been proposed that merge clinical data with mathematical models of varying complexity to obtain an indirect approximation to patient-specific myocardial properties. These range from established chamber stiffness estimates derived from pressurevolume curves or wall stress surrogates to developing Address correspondence to Myrianthi Hadjicharalambous, Division of Imaging Sciences and Biomedical Engineering, King’s College London, St. Thomas’ Hospital, London SE1 7EH, UK. Electronic mail: myrianthi.hadjicharalambous@kcl.ac.uk Annals of Biomedical Engineering, Vol. 45, No. 3, March 2017 ( 2016) pp. 605–618 DOI: 10.1007/s10439-016-1721-4 0090-6964/17/0300-0605/
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Alternate Webpage(s)	https://research-information.bris.ac.uk/files/202425911/Full_text_PDF_final_published_version_.pdf
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Language	English
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Resource Type	Article