Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.

• Tác giả: Ali Ashek, Wenjia Bai, Nicoleta Baxan, Timothy J W Dawes, Jinming Duan, Chen Nien-Chen, Marili Niglas, Declan O'Regan, Chongyang Xie, Lan Zhao, Lin Zhao
• Ngôn ngữ: eng
• Ký hiệu phân loại: 621.3857 Electrical, magnetic, optical, communications, computer engineering; electronics, lighting
• Thông tin xuất bản: United States : Pulmonary circulation , 2025
• Bộ sưu tập: NCBI
• ID: 745972

Tóm tắt

 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.