3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices
Journal:
arXiv
Published Date:
Jul 3, 2025
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.
