Research output: Contribution to journal › Article › peer-review
Myrianthi Hadjicharalambous, Liya Asner, Radomir Chabiniok, Eva Sammut, James Wong, Devis Peressutti, Eric Kerfoot, Andrew King, Jack Lee, Reza Razavi, Nicolas Smith, Gerald Carr-White, David Nordsletten
Original language | English |
---|---|
Number of pages | 14 |
Journal | Annals of Biomedical Engineering |
DOIs | |
E-pub ahead of print | 7 Sep 2016 |
Non-invasive Model-Based Assessment_HADJICHARALAMBOUS_First online 7Sep2016_GOLD VoR
Non_invasive_Model_Based_Assessment_HADJICHARALAMBOUS_First_online_7Sep2016_GOLD_VoR.pdf, 2.24 MB, application/pdf
Uploaded date:23 Sep 2016
Version:Final published version
Licence:CC BY
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 ([Formula: see text]) and healthy volunteers ([Formula: see text]). 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 non-invasive 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.
King's College London - Homepage
© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454