Efficient identification of myocardial material parameters and the stress-free reference configuration for patient-specific human heart models

TL;DR

This paper introduces a fast, automated method for identifying passive myocardial material parameters and stress-free configurations in patient-specific heart models, enhancing clinical applicability and robustness.

Contribution

The study presents a novel, efficient algorithm that automatically fits cardiac tissue parameters and generates stress-free configurations using minimal clinical data, applicable across various FE models.

Findings

Algorithm accurately fits material parameters to patient data.

Method is robust to initial parameter guesses.

Applicable to different constitutive laws and FE formulations.

Abstract

Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, a stress-free reference configuration is generated, where a novel fail-safe feature to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters.