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Updated: Jun 8, 2026
09:20
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
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Statistical Atlas-Based Surrogate Model of Biventricular Wall Mechanics
Alejandra Robles1, Raksha Konanur2, Anna Qi1
1Department of Bioengineering, University of California San Diego, USA.
Biorxiv : the Preprint Server for Biology
|January 16, 2026
Summary
This study developed a fast surrogate model for predicting heart mechanics using AI. It accurately simulates ventricular wall motion, offering a quicker alternative to complex finite element analysis for personalized cardiac modeling.
Area of Science:
- Computational mechanics
- Biomedical engineering
- Medical imaging analysis
Background:
- Finite element (FE) analysis is crucial for understanding ventricular wall mechanics but is computationally intensive.
- Existing methods for cardiac modeling often lack the speed required for rapid, subject-specific predictions.
Purpose of the Study:
- To develop a computationally efficient surrogate model for predicting biventricular wall mechanics.
- To enable rapid, subject-specific cardiac modeling by replacing complex FE simulations.
Main Methods:
- Utilized a statistical atlas of biventricular shapes from the UK Biobank.
- Employed Holzapfel-Ogden constitutive law and a time-varying elastance model for FE simulations.
- Trained a multi-layer perceptron surrogate model using principal components of simulated deformations.
Main Results:
- The surrogate model achieved high fidelity, with mean square error in predicted displacements < 2 mm.
- Volumetric overlaps between predicted and FE-simulated shapes exceeded 97%.
- Demonstrated accurate prediction of ventricular wall mechanics.
Conclusions:
- The developed surrogate model offers a feasible method for rapid, subject-specific cardiac modeling.
- This approach bypasses the need for computationally expensive finite element analysis.
- Enables efficient prediction of ventricular wall mechanics for clinical applications.
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