Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks

Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D...

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Vydáno v:IEEE journal of biomedical and health informatics Ročník 28; číslo 8; s. 4810 - 4819
Hlavní autoři: Beetz, Marcel, Banerjee, Abhirup, Grau, Vicente
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.08.2024
Témata:
Anatomy
Cardiac disease
Cardiac mechanics
cardiac motion
cine MRI
Deep Learning
Deformable models
Deformation
geometric deep learning
Heart - diagnostic imaging
Heart - physiology
Humans
Imaging, Three-Dimensional - methods
Magnetic resonance imaging
Male
Models, Cardiovascular
Myocardial Contraction - physiology
myocardial infarction
Point cloud compression
point clouds
Shape
subpopulation-specific 3D deformation
survival analysis
Three-dimensional displays
ISSN:2168-2194, 2168-2208, 2168-2208
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Shrnutí:Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings. In this work, we present the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach for direct modeling of 3D cardiac mechanics of the biventricular anatomy between the extreme ends of the cardiac cycle. Its encoder-decoder architecture combines a low-dimensional latent space with recent advances in point cloud deep learning for effective multi-scale feature learning directly on flexible and memory-efficient point cloud representations of the cardiac anatomy. We first evaluate the PCD-Net's predictive capability for both cardiac contraction and relaxation on a large UK Biobank dataset of over 10,000 subjects and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. We then show the PCD-Net's ability to capture subpopulation-specific differences in 3D cardiac mechanics between normal and myocardial infarction (MI) subjects and visualize abnormal phenotypes between predicted normal 3D shapes and corresponding observed ones. Finally, we demonstrate that the PCD-Net's learned 3D deformation encodings outperform multiple clinical and machine learning benchmarks by 11% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2024.3389871