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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...
PPCAR-Net is a neural network that reconstructs 3‑D coronary artery centrelines and radii from a sparse set of X‑ray angiographic views. It predicts a branch‑structured centreline-and‑radius representation directly, avoiding explicit point matching or triangulation. The method refines geometry and radius estimates using projection‑guided refiners and achieves real‑time performance with strong centreline accuracy, especially for the right coronary artery.
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.
The paper introduces VasoMIM, a vascular anatomy‑aware masked image modeling framework for X‑ray angiogram analysis, which uses an anatomy‑guided masking strategy and an anatomical consistency loss to learn robust vascular representations. It also presents XA‑170K, the largest pre‑training dataset for X‑ray angiograms. Experiments on six datasets and four downstream tasks show that VasoMIM outperforms existing methods and achieves state‑of‑the‑art performance.
arXiv:2610.20509v1 Announce Type: cross Abstract: Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing...
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.