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
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
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...
EPC‑RawDC is a 3‑D latent diffusion model that synthesises CT images from CBCT scans by enforcing two physics‑based constraints: a geometry‑equivariant projection loss and raw data consistency using scan‑specific CBCT projections. Trained on two clinical cohorts with different scanner geometries, the method achieved SSIM of 0.97 and improved PSNR and MAE over a baseline DDPM, reducing projection and equivariant errors by 55 % and 41 % respectively.
By Alzahra Altalib, Chunhui Li, Alessandro Perelli
arXiv:2608. 19932v1 Announce Type: new Abstract: Public cardiac cohorts annotate different subsets of the heart, so shapes from separate sources cannot be pooled without shared correspondence.
By Matej Gazda, Jakub Gazda, Juraj Gazda, Peter Drotar
arXiv:2605.30972v2 Announce Type: replace
Abstract: Accurate 3D medical image segmentation requires both fine spatial detail and long-range volumetric context. Although Mamba provides efficient long-...
By Bakht Zada, Chao Tong, Qile Su, Shuai Zhang
The paper introduces a geometry‑guided sampling operator that directs feature sampling rather than altering convolution kernels in 3D encoder‑decoder networks. By predicting local orientations and bounded step sizes, the operator samples symmetrically around each voxel, generating compact geometric and boundary cues that improve fine‑structure segmentation. Replacing stride‑1 and stride‑2 operations in a 3D U‑Net yields consistent gains on BraTS, MSD Hepatic Vessel, and TDSC‑ABUS datasets, with better boundary metrics and fewer parameters, and the operator can be integrated into other backbones without architectural changes.
By Sizhe Wang, Himashi Peiris, Zhaolin Chen