@misc {42561,
	title = {Functional Analysis of Healthy and Heart Failure Tissue Populations using 3D Cardiac Electromechanical Models},
	howpublished = {15th World Congress on Computational Mechanics (virtual)},
	year = {2022},
	abstract = {Computer modelling and simulation of the beating heart must reflect on electrical activation of cells and tissue, mechanical properties of tissue, and their interaction. Electrophysiological properties of the heart have been simulated abundantly and applied to increase mechanistic understanding of disease and improve treatment development. Recent maturity in cardiac mechanical modelling increased quantitative predictive power. However, tight coupling of cardiac mechanics with the underlying electrophysiological properties of the tissue makes modelling clinical mechanical phenomena such as cardiac disease and drug effects difficult. A combination of uncertainty quantification through populations of models and fully coupled electromechanics models provide greater predictive power on cardiac mechanisms and aid treatment development for cardiac diseases.\&nbsp;A fully coupled electromechanics model of ventricular tissue is developed by coupling the O{\textquoteright}Hara-Rudy[1] electrophysiology model and the Land[2] mechanics model. A population of models was created by varying 16 electrophysiological and 11 mechanical parameters at the cell level. The population was calibrated from 1000 to 187 models based on biomarkers derived from the action potential shape, calcium transient and active tension. This calibrated population was altered in 11 parameters of the cell model to represent heart failure (based on [3]). A geometry of 20x7x3 mm is simulated with 1.0 or 0.5 mm spatial resolution for mechanics or electrophysiology respectively, with free movement in the fibre direction on one side. Results are extracted at the centre of the tissue, as well as tissue shortening on the free-contracting side.\&nbsp;Relative to the healthy population, heart failure manifests in both electrophysiological and mechanics biomarkers. The action potential takes ~20\% longer to recover from activation and peak calcium concentration in the cell is reduced by 50.9\%. In a similar trend, mechanical biomarkers show 42.2\% reduction in peak active force, slower contraction and more variation in recovery times across the heart failure tissue population. The peak tissue shortening in the fibre direction as a result of free contraction is reduced from 0.68{\textpm}0.07mm to 0.45{\textpm}0.07mm (-33.2\%) and its peak is delayed by 112ms (38.6\%) in heart failure.\&nbsp;These simulations indicate strong effects on electrophysiological and mechanical heart function by population variation and disease such as heart failure. Therefore, strongly coupled 3D models are necessary to assess the impact of biological variation, cardiac vulnerability as well as safe and effective treatment development.REFERENCES[1]\&nbsp;\&nbsp;\&nbsp;\&nbsp; O{\textquoteright}Hara, T. et al.; PLoS Comput. Biol. 2011 [doi:10.1371/journal.pcbi.1002061][2]\&nbsp;\&nbsp;\&nbsp;\&nbsp; Land, S. et al.; J. Mol. Cell. Cardiol. 2017 [doi:10.1016/J.YJMCC.2017.03.008][3]\&nbsp;\&nbsp;\&nbsp;\&nbsp; Gomez, J. et al; PLoS ONE 2014 [doi:10.1371/journal.pone.0106602]},
	keywords = {Cardiac biomechanics, Cardiac disease, Uncertainty quantification},
	author = {Ilsbeth Gerarda Ma {van Herck} and Finsberg, Henrik and Daversin-Catty, C{\'e}cile and Mora, Maria and Trenor, Beatriz and Arevalo, Hermenegild and Wall, Samuel}
}
@article {26192,
	title = {Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge},
	journal = {Cardiovascular Engineering and Technology},
	volume = {9},
	year = {2018},
	month = {10/2018},
	pages = {544{\textendash}564},
	publisher = {Springer},
	address = {US},
	abstract = {PurposeImage-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual\&nbsp;groups,\&nbsp;have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline.Methods3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters.ResultsA total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience.\&nbsp;Wide\&nbsp;variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56\%, which reduced to \&lt; 30\% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed\&nbsp;only\&nbsp;modest consensus among teams.\&nbsp;Experience\&nbsp;was not a significant predictor of variability.ConclusionsWide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.},
	keywords = {Intracranial aneurysm, Patient-specific modeling, Rupture risk, Uncertainty quantification, Wall shear stress},
	issn = {1869-408X},
	doi = {10.1007/s13239-018-00374-2},
	url = {https://goo.gl/mG9u9t},
	author = {Valen-Sendstad, Kristian and Bergersen, Aslak and Shimogonya, Yuji and Goubergrits, Leonid and Bruening, Jan and Pallares, Jordi and Cito, Salvatore and Piskin, Senol and Pekkan, Kerem and Arjan J. {Geers} and Larrabide, Ignacio and Rapaka, Saikiran and Mihalef, Viorel and Fu, Wenyu and Qiao, Aike and Jain, Kartik and Roller, Sabine and Mardal, Kent-Andre and Kamakoti, Ramji and Spirka, Thomas and Ashton, Neil and Revell, Alistair and Aristokleous, Nicolas and Houston, Graeme and Tsuji, Masanori and Ishida, Fujimaro and Prahlad G. {Menon} and Leonard D. {Browne} and Broderick, Stephen and Shojima, Masaaki and Koizumi, Satoshi and Barbour, Michael and Aliseda, Alberto and Hern{\'a}n G. {Morales} and Lef{\`e}vre, Thierry and Hodis, Simona and Yahia M. {Al-Smadi} and Justin S. {Tran} and Alison L. {Marsden} and Vaippummadhom, Sreeja and Einstein, Albert and Alistair G. {Brown} and Debus, Kristian and Niizuma, Kuniyasu and Rashad, Sherif and Sugiyama, Shin-ichiro and Owais Mohammad {Khan} and Adam R. {Updegrove} and Shawn C. {Shadden} and Bart M. W. {Cornelissen} and Charles B. L. M. {Majoie} and Ber, Philipp and Saalfield, Sylvia and Kono, Kenichi and David A. {Steinmam}}
}
