The paper presents a multi‑task neural network and scalar‑potential solver for reconstructing bilateral coronary artery trees from cardiac CT angiography. It operates on $128^3$ regions of interest, using a 3D pyramid encoder, tubular decoder, and specialized heads for sub‑voxel displacement, ipsilateral territory prediction, and ostium localization. The method achieves sub‑millimetre accuracy on out‑of‑sample and development datasets, outperforming baseline 3D U‑Net models.
By Zeyu Jia, Xin Ming
arXiv:2608. 12274v1 Announce Type: cross Abstract: Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.
By Rafi Ibn Sultan, Chengyin Li, Yiannos Demetriou, Ahmed I. Ghanem, Joshua P. Kim, Justine Cunningham, Hassan Bagher-Ebadian, Dongxiao Zhu, Kundan S. Thind
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.
By Yu Ren, Hwee Kuan Lee, Tat-Jen Cham, Jonathan Yap, Khung Keong Yeo
arXiv:2606. 13188v1 Announce Type: cross Abstract: Building patient-specific cardiac models sits at the heart of precision cardiology, yet getting those models into clinical use keeps running into the same wall: mesh generation is slow, messy, and frustrating.
By Abhishek H S, Akash Ganamukhi, Abhimanyu Suresh, Aditya G Hiremath, Prasad B Honnavalli, Adithya Balasubramanyam
EUReCA is a geometry‑aware, feed‑forward model that reconstructs cone‑beam CT images from 8–100 projections without needing view‑specific retraining. It encodes each projection with a shared multiscale backbone, back‑projects features into 3‑D grids, aggregates variable views at fixed cost, and refines the volume using a global transformer and decoder. On simulated and real patient data, EUReCA outperforms existing feed‑forward and iterative methods, achieving high PSNR and superior performance across all tested view counts.
By Jiening Zhu, Chengzhu Zhang, Jason Fan, LiCheng Kuo, Weixing Cai, Xiuxiu He, Laura Cervino, Jean Moran, Xiang Li, Tianfang Li, Yabo Fu
arXiv:2606. 00109v1 Announce Type: cross Abstract: Coronary guidewire endpoint localization is a fundamental capability for computer-assisted PCI, and its importance increases as robot-assisted PCI is progressively adopted to reduce operator radiation exposure.
By Haoyuan Tang, Zhuo Zhang, Jialin Li, Shuai Xiao, Jiachen Yang