Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views

TL;DR

This paper introduces an optimized Pix2Vox++ based method for reconstructing 3D cardiac anatomy from limited 2D echocardiogram views, enabling accurate, efficient, and less operator-dependent heart imaging.

Contribution

It presents a memory-efficient modification to Pix2Vox++ and a pipeline for 3D reconstruction from 2D cardiac views, advancing ultrasound-based cardiac imaging.

Findings

Achieved peak IoU > 0.88 on synthetic data

Demonstrated effective 3D reconstruction from two 2D views

Presented preliminary results on real echo images

Abstract

Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is the primary modality for cardiac imaging, however acquisition requires high operator skill, and its interpretation and analysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3D can enable discovery of new biomarkers and make imaging less dependent on operator expertise, however most ultrasound systems only have 2D imaging capabilities. We propose both a simple alteration to the Pix2Vox++ networks for a sizeable reduction in memory usage and computational complexity, and a pipeline to perform reconstruction of 3D anatomy from 2D standard cardiac views, effectively enabling 3D anatomical reconstruction from limited 2D data. We evaluate our pipeline using synthetically generated data achieving accurate 3D whole-heart reconstructions (peak intersection over union score > 0.88) from just two standard anatomical 2D views of the heart. We also show preliminary results using real echo images.