Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo in Cardiac Electrophysiology

Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption.

Source: Medical Image Analysis - May 17, 2018 Category: Radiology Authors: Jwala Dhamala, Hermenegild J. Arevalo, John Sapp, B. Milan Hor ácek, Katherine C. Wu, Natalia A. Trayanova, Linwei Wang Source Type: research