Related Experiment Video
Updated: May 7, 2026

In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Isolated effect of geometry on mitral valve function for in silico model development
Andrew William Siefert1, Jean-Pierre Michel Rabbah, Neelakantan Saikrishnan
1a The Wallace H. Coulter Department of Biomedical Engineering , Georgia Institute of Technology and Emory University , 311 Ferst Dr., Atlanta , GA 30332 , USA.
Abstract:
Computational models for the heart's mitral valve (MV) exhibit several uncertainties that may be reduced by further developing these models using ground-truth data-sets. This study generated a ground-truth data-set by quantifying the effects of isolated mitral annular flattening, symmetric annular dilatation, symmetric papillary muscle (PM) displacement and asymmetric PM displacement on leaflet coaptation, mitral regurgitation (MR) and anterior leaflet strain. MVs were mounted in an in vitro left heart simulator and tested under pulsatile haemodynamics. Mitral leaflet coaptation length, coaptation depth, tenting area, MR volume, MR jet direction and anterior leaflet strain in the radial and circumferential directions were successfully quantified at increasing levels of geometric distortion. From these data, increase in the levels of isolated PM displacement resulted in the greatest mean change in coaptation depth (70% increase), tenting area (150% increase) and radial leaflet strain (37% increase) while annular dilatation resulted in the largest mean change in coaptation length (50% decrease) and regurgitation volume (134% increase). Regurgitant jets were centrally located for symmetric annular dilatation and symmetric PM displacement. Asymmetric PM displacement resulted in asymmetrically directed jets. Peak changes in anterior leaflet strain in the circumferential direction were smaller and exhibited non-significant differences across the tested conditions. When used together, this ground-truth data-set may be used to parametrically evaluate and develop modelling assumptions for both the MV leaflets and subvalvular apparatus. This novel data may improve MV computational models and provide a platform for the development of future surgical planning tools.
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