# An Optimization Framework to Personalize Passive Cardiac Mechanics

arXiv: https://arxiv.org/abs/2404.02807 · HTML: https://mlsanity.com/paper/2404.02807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 99 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations](https://mlsanity.com/paper/2203.10517/) (2022)
- [A comprehensive and biophysically detailed computational model of the whole human heart electromechanics](https://mlsanity.com/paper/2207.12460/) (2022)
- [Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations](https://mlsanity.com/paper/2101.10960/) (2021)
- [The importance of the pericardium for cardiac biomechanics: From physiology to computational modeling](https://mlsanity.com/paper/1810.05451/) (2018)
- [Branched Latent Neural Maps](https://mlsanity.com/paper/2308.02599/) (2023)
- [Automatic Mapping of Atrial Fiber Orientations for Patient-Specific Modeling of Cardiac Electromechanics using Image-Registration](https://mlsanity.com/paper/1812.02587/) (2018)

## Abstract (arXiv)

Personalized cardiac mechanics modeling is a powerful tool for understanding the biomechanics of cardiac function in health and disease and assisting in treatment planning. However, current models are limited to using medical images acquired at a single cardiac phase, often limiting their applicability for processing dynamic image acquisitions. This study introduces an inverse finite element analysis (iFEA) framework to estimate the passive mechanical properties of cardiac tissue using time-dependent medical image data. The iFEA framework relies on a novel nested optimization scheme, in which the outer iterations utilize a traditional optimization method to best approximate material parameters that fit image data, while the inner iterations employ an augmented Sellier's algorithm to estimate the stress-free reference configuration. With a focus on characterizing the passive mechanical behavior, the framework employs structurally based anisotropic hyperelastic constitutive models and physiologically relevant boundary conditions to simulate myocardial mechanics. We use a stabilized variational multiscale formulation for solving the governing nonlinear elastodynamics equations, verified for cardiac mechanics applications. The framework is tested in myocardium models of biventricle and left atrium derived from cardiac phase-resolved computed tomographic (CT) images of a healthy subject and three patients with hypertrophic obstructive cardiomyopathy (HOCM). The impact of the choice of optimization methods and other numerical settings, including fiber direction parameters, mesh size, initial parameters for optimization, and perturbations to optimal material parameters, is assessed using a rigorous sensitivity analysis. The performance of the current iFEA is compared against an assumed power-law-based pressure-volume relation, typically used for single-phase image acquisition.