3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices

TL;DR

This paper introduces a novel framework for reconstructing personalized 3D heart models from sparse 2D echocardiographic slices, significantly improving volume estimation accuracy and enabling RV volume assessment from standard clinical images.

Contribution

The proposed method innovatively combines pose estimation and neural implicit modeling to reconstruct 3D heart anatomy from limited 2D echo slices, advancing clinical cardiac imaging.

Findings

Significantly reduces LV volume estimation error from 20.24% to 1.98%.

Enables accurate RV volume estimation with 5.75% error from 2D slices.

Validated on two datasets demonstrating clinical potential.

Abstract

Echocardiography (echo) plays an indispensable role in the clinical practice of heart diseases. However, ultrasound imaging typically provides only two-dimensional (2D) cross-sectional images from a few specific views, making it challenging to interpret and inaccurate for estimation of clinical parameters like the volume of left ventricle (LV). 3D ultrasound imaging provides an alternative for 3D quantification, but is still limited by the low spatial and temporal resolution and the highly demanding manual delineation. To address these challenges, we propose an innovative framework for reconstructing personalized 3D heart anatomy from 2D echo slices that are frequently used in clinical practice. Specifically, a novel 3D reconstruction pipeline is designed, which alternatively optimizes between the 3D pose estimation of these 2D slices and the 3D integration of these slices using an implicit neural network, progressively transforming a prior 3D heart shape into a personalized 3D heart model. We validate the method with two datasets. When six planes are used, the reconstructed 3D heart can lead to a significant improvement for LV volume estimation over the bi-plane method (error in percent: 1.98\% VS. 20.24\%). In addition, the whole reconstruction framework makes even an important breakthrough that can estimate RV volume from 2D echo slices (with an error of 5.75\% ). This study provides a new way for personalized 3D structure and function analysis from cardiac ultrasound and is of great potential in clinical practice.