Automated bi‐ventricular segmentation and regional cardiac wall motion analysis for rat models of pulmonary hypertension
Author(s)
Type
Journal Article
Abstract
Artificial intelligence-based cardiac motion mapping offers predictive insights into pulmonary hypertension (PH) disease progression and its impact on the heart. We proposed an automated deep learning pipeline for bi-ventricular segmentation and 3D wall motion analysis in PH rodent models for bridging the clinical developments. A data set of 163 short-axis cine cardiac magnetic resonance scans were collected longitudinally from monocrotaline (MCT) and Sugen-hypoxia (SuHx) PH rats and used for training a fully convolutional network for automated segmentation. The model produced an accurate annotation in < 1 s for each scan (Dice metric > 0.92). High-resolution atlas fitting was performed to produce 3D cardiac mesh models and calculate the regional wall motion between end-diastole and end-systole. Prominent right ventricular hypokinesia was observed in PH rats (−37.7% ± 12.2 MCT; −38.6% ± 6.9 SuHx) compared to healthy controls, attributed primarily to the loss in basal longitudinal and apical radial motion. This automated bi-ventricular rat-specific pipeline provided an efficient and novel translational tool for rodent studies in alignment with clinical cardiac imaging AI developments.
Date Issued
2025-04-01
Date Acceptance
2025-04-23
Citation
Pulmonary Circulation, 2025, 15 (2)
ISSN
2045-8940
Publisher
Wiley
Start Page
e70092
Journal / Book Title
Pulmonary Circulation
Volume
15
Issue
2
Copyright Statement
© 2025 The Authors. Pulmonary Circulation published by Wiley Periodicals LLC on behalf of the Pulmonary Vascular Research Institute. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40356847
PII: PUL270092
Subjects
3D motion analysis
cardiovascular disease
deep learning
magnetic resonance imaging
pulmonary hypertension
Publication Status
Published
Coverage Spatial
United States
Article Number
e70092
Date Publish Online
2025-05-12