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

Frontiers in Physiology
Paolo Di AchilleViatcheslav Gurev

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 of biophysical FEM simulations. The first statistical model estimates unloaded configurations based on either the intraventricular pressure or the end-diastolic fiber strain. The technique provides an alternative to the standard fixed-point iteration algorithm, which is more computationally expensive when used to unload more than 10 ventricles. The second statistical model captures the effects of varying infarct geometries on cardiac output. F...Continue Reading

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Citations

Jan 7, 2020·PloS One·Paolo Di AchilleViatcheslav Gurev
Nov 26, 2019·Journal of the Royal Statistical Society. Series C, Applied Statistics·Vinny DaviesDirk Husmeier
May 26, 2020·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·J O CamposB M Rocha
Jan 7, 2020·Frontiers of Physics·Yaghoub DabiriJulius M Guccione
Nov 2, 2021·Frontiers in Physiology·Gonzalo D Maso TalouMartyn P Nash

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Methods Mentioned

BETA
imaging techniques

Software Mentioned

TriSeg
SciPy

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