Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics.

ABSTRACT: Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients. Statistical distributions of geometric parameters are then used to design parametric studies investigating the effects of: (1) passive material properties during ventricular filling, and (2) infarct geometry on ventricular contraction in patients after a heart attack. Gaussian Process regression is used in both cases to build statistical models trained on the results

SUBMITTER: Di Achille P 

PROVIDER: S-EPMC6102646 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature