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Filling the Gaps: Generating 4D Dense Cardiac Anatomy from Sparse CMR for Enhanced Tetralogy of Fallot Assessment
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DOI：10.1016/j.jocmr.2026.102765.png)
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Abstract
En 中文
Accurate assessment of 3D four chamber cardiac anatomy is essential for managing repaired Tetralogy of Fallot (rToF), yet standard cardiac magnetic resonance (CMR) protocols acquire sparse 2D slices with anisotropic resolution, inter-slice inconsistencies, and motion artifacts from patient movement and incomplete acquisitions. As CT provides isotropic 3D whole-heart segmentations, it can be used to train deep learning models to reconstruct dense 3D anatomies from sparse CMR-like slices simulated from these volumes, bridging the gap between CMR's clinical accessibility and CT's spatial resolution. We aimed to develop and validate such a deep learning pipeline for comprehensive 3D whole-heart reconstruction from routine 2D cine short and long axis CMR images in rToF patients.
Keywords:
LV
left ventricle
RV
right ventricle
LA
left atrium
RA
right atrium
LCN
label completion network
CHD
congenital heart disease
(r)TOF
(repaired-)Tetralogy of Fallot
NCE-MRA
Non-contrast enhanced magnetic resonance angiography
Congenital heart disease
Cardiovascular magnetic resonance
Deep learning
Image segmentation
Tetralogy of Fallot
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