S2MNet: Speckle-To-Mesh Net for Three-Dimensional Cardiac Morphology Reconstruction via Echocardiogram

TL;DR

S2MNet is a deep learning framework that reconstructs detailed 3D cardiac models from six 2D echocardiogram slices, overcoming data acquisition challenges and ensuring high-fidelity, artifact-free 3D reconstructions validated by clinical correlation.

Contribution

The paper introduces S2MNet, a novel deep learning approach that reconstructs 3D heart models from 2D echocardiogram slices using a deformation field method, reducing data collection difficulties.

Findings

High correlation between estimated LV volume and clinical measurement GLPS

Effective avoidance of spatial discontinuities in 3D reconstructions

Validated on clinically collected echocardiogram data

Abstract

Echocardiogram is the most commonly used imaging modality in cardiac assessment duo to its non-invasive nature, real-time capability, and cost-effectiveness. Despite its advantages, most clinical echocardiograms provide only two-dimensional views, limiting the ability to fully assess cardiac anatomy and function in three dimensions. While three-dimensional echocardiography exists, it often suffers from reduced resolution, limited availability, and higher acquisition costs. To overcome these challenges, we propose a deep learning framework S2MNet that reconstructs continuous and high-fidelity 3D heart models by integrating six slices of routinely acquired 2D echocardiogram views. Our method has three advantages. First, our method avoid the difficulties on training data acquasition by simulate six of 2D echocardiogram images from corresponding slices of a given 3D heart mesh. Second, we introduce a deformation field-based method, which avoid spatial discontinuities or structural artifacts in 3D echocardiogram reconstructions. We validate our method using clinically collected echocardiogram and demonstrate that our estimated left ventricular volume, a key clinical indicator of cardiac function, is strongly correlated with the doctor measured GLPS, a clinical measurement that should demonstrate a negative correlation with LVE in medical theory. This association confirms the reliability of our proposed 3D construction method.