Neural Implicit 3D Cardiac Shape Reconstruction from Sparse CT Angiography Slices Mimicking 2D Transthoracic Echocardiography Views
Gino E. Jansen, Carolina Br\'as, R. Nils Planken, Mark J. Schuuring, Berto J. Bouma, Ivana I\v{s}gum

TL;DR
This paper introduces a neural implicit method for reconstructing accurate 3D cardiac shapes from sparse 2D-like CTA slices, mimicking standard echocardiography views, with promising results for clinical application.
Contribution
The novel approach reconstructs 3D cardiac structures from sparse, TTE-mimicking CTA planes using neural implicit functions, outperforming traditional volume estimation methods.
Findings
Achieved an average Dice coefficient of 0.86 across structures.
Lower volume errors compared to Simpson's biplane rule.
Successfully reconstructed 3D shapes from simulated TTE views.
Abstract
Accurate 3D representations of cardiac structures allow quantitative analysis of anatomy and function. In this work, we propose a method for reconstructing complete 3D cardiac shapes from segmentations of sparse planes in CT angiography (CTA) for application in 2D transthoracic echocardiography (TTE). Our method uses a neural implicit function to reconstruct the 3D shape of the cardiac chambers and left-ventricle myocardium from sparse CTA planes. To investigate the feasibility of achieving 3D reconstruction from 2D TTE, we select planes that mimic the standard apical 2D TTE views. During training, a multi-layer perceptron learns shape priors from 3D segmentations of the target structures in CTA. At test time, the network reconstructs 3D cardiac shapes from segmentations of TTE-mimicking CTA planes by jointly optimizing the latent code and the rigid transforms that map the observed…
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