Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twins

TL;DR

This paper introduces a weakly supervised learning approach to reconstruct personalized 4D heart models from cine MRI data, enabling detailed cardiac analysis for digital twins in precision medicine.

Contribution

It presents a novel method for directly generating 4D heart meshes from multi-view cine MRI using self-supervised learning, advancing personalized cardiac modeling.

Findings

Accurately reconstructs 4D heart meshes from cine MRI data.

Enables extraction of key cardiac variables like ejection fraction.

Demonstrates feasibility of personalized 4D cardiac modeling from MRI.

Abstract

Cardiac digital twins (CDTs) provide personalized in-silico cardiac representations and hold great potential for precision medicine in cardiology. However, whole-heart CDT models that simulate the full organ-scale electromechanics of all four heart chambers remain limited. In this work, we propose a weakly supervised learning model to reconstruct 4D (3D+t) heart mesh directly from multi-view 2D cardiac cine MRIs. This is achieved by learning a self-supervised mapping between cine MRIs and 4D cardiac meshes, enabling the generation of personalized heart models that closely correspond to input cine MRIs. The resulting 4D heart meshes can facilitate the automatic extraction of key cardiac variables, including ejection fraction and dynamic chamber volume changes with high temporal resolution. It demonstrates the feasibility of inferring personalized 4D heart models from cardiac MRIs, paving the way for an efficient CDT platform for precision medicine. The code will be publicly released once the manuscript is accepted.