X-ray coronary angiography is the clinical gold standard for coronary artery disease during real-time cardiac interventions, but provides only 2D projections of inherently 3D vessels. Existing learnin...
arXiv:2609.15550v1 Announce Type: cross
Abstract: X-ray coronary angiography is the clinical gold standard for coronary artery disease during real-time cardiac interventions, but provides only 2D pro...
By Deyu Meng, Mojtaba Lashgari, Yiying Wang, Abhirup Banerjee
Multi-view reasoning in coronary X-ray angiography is inherently a cross-projection geometric problem, yet automated report generation in this setting remains largely unexplored. The 3D vascular topology leads to projection-dependent branch overlap and foreshortening, rendering single-view modeling fundamentally incomplete and unstable for lesion localization and stenosis grading.
The paper introduces a physics-informed deep learning framework that reconstructs 3D coronary geometry from dual-view angiography and predicts velocity and pressure fields using a decoupled network with embedded physical priors. Across 32 patients and four flow conditions, the model achieved a trans‑stenotic pressure‑drop error of 2.02% and velocity/pressure relative‑L2 errors of 0.054 and 0.023, respectively, while matching hospital‑measured FFR with 93.8% diagnostic accuracy. The pipeline completes the full angiography‑to‑hemodynamics conversion in about 20 minutes per patient and supports sparse‑data assimilation for revascularization planning.
By Xi Chen, Jianchuan Yang, Hongde Li, Guangxin He, Qiuyu Ye, Qiang Luo, Mao Chen, Wenqi Hu
Local2Mesh is a spatially localized contour‑to‑mesh framework that reconstructs 3‑D left ventricular geometry from sparse 2‑D cardiac MRI contours without requiring 3‑D mesh annotations. It corrects inter‑slice misalignment through geometry‑aware alignment and routes contour features to template vertices via a plane‑aware Local Router, then jointly uses local and global features to deform a template mesh. Experiments on M&Ms‑2 and ACDC datasets show superior geometric reconstruction, functional estimation, and disease classification compared to existing methods, with strong cross‑dataset generalization.
By Haoyu Wu, Ling Lin, Pascal Lef\`evre, Ruizhe Li, Xiaowu Sun
The paper presents an anatomy‑aligned surface learning framework for reconstructing patient‑specific 4D myocardial surfaces from sparsely sampled short‑axis cine MRI. By parameterizing epicardial and endocardial surfaces on a shared circumferential‑longitudinal UV domain, the method transforms irregular 3D reconstruction into structured coordinate‑field completion, enabling explicit correspondence across subjects and cardiac phases. Experiments on three public datasets show the approach outperforms mesh‑based and implicit methods, achieving Chamfer distances around 2.6–2.9 mm and preserving ventricular function with small errors in volume and ejection fraction.
By Xiaohan Yuan, Xuan Yang, Qingya Li, Yangang Wang, Lei Li