2018/01/08 by Rocío Rodríguez‐Cantano, Rodríguez-Cantano, Rocío, Joakim Sundnes +3
Decision Sciences · Engineering · Medicine · #Automotive and Human Injury Biomechanics #Computational Physics (physics.comp-ph) #Elasticity and Material Modeling #FOS: Physical sciences #Probabilistic and Robust Engineering Design
paper · pdf · doi:10.48550/arxiv.1801.02989
openalex publication_date 2018/01/08 · openalex created_date 2022/09/28 · openalex updated_date 2026/07/28
Computational cardiac modelling is a mature area of biomedical computing, and\nis currently evolving from a pure research tool to aiding in clinical decision\nmaking. Assessing the reliability of computational model predictions is a key\nfactor for clinical use, and uncertainty quantification (UQ) and sensitivity\nanalysis are important parts of such an assessment. In this study, we apply new\nmethods for UQ in computational heart mechanics to study uncertainty both in\nmaterial parameters characterizing global myocardial stiffness and in the local\nmuscle fiber orientation that governs tissue anisotropy. The uncertainty\nanalysis is performed using the polynomial chaos expansion (PCE) method, which\nis a non-intrusive meta-modeling technique that surrogates the original\ncomputational model with a series of orthonormal polynomials over the random\ninput parameter space. In addition, in order to study variability in the muscle\nfiber architecture, we model the uncertainty in orientation of the fiber field\nas an approximated random field using a truncated Karhunen-Lo 'eve expansion.\nThe results from the UQ and sensitivity analysis identify clear differences in\nthe impact of various material parameters on global output quantities.\nFurthermore, our analysis of random field variations in the fiber architecture\ndemonstrate a substantial impact of fiber angle variations on the selected\noutputs, highlighting the need for accurate assignment of fiber orientation in\ncomputational heart mechanics models.\n