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arXiv Computer Vision

Sub-Voxel Displacement and Ipsilateral Territory Decoupling for 3D Bilateral Coronary Artery Tree Reconstruction in Cardiac CT Angiography

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.

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arXiv Computer Vision
Oct 28

Tri-Directional Sub-Voxel Displacement and Ipsilateral Territory Decoupling for 3D Bilateral Coronary Artery Tree Reconstruction in Cardiac CT Angiography

The paper introduces a novel 3‑D reconstruction framework for bilateral coronary artery trees from cardiac CT angiography, addressing limitations such as voxel quantization, territory ambiguity, and ostium drift. It employs a canonical SO(3) multi‑scale representation, a Riemannian arborescence solver, and a sub‑voxel displacement head, training 327,385 active parameters in a single 30‑epoch AdamW loop. On unseen datasets, the method achieves sub‑millimetre accuracy in centerline distance and ostial localisation, outperforming prior approaches.

By Zeyu Jia, Xin Ming
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arXiv AI
Aug 13

A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery

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
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arXiv AI
Jun 12

Transformer-Guided Graph Attention for Direct Cardiac Mesh Reconstruction: A Structural Digital Twin Framework

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
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arXiv Computer Vision
Aug 27

Steer the Sampling, Not the Kernel Grid: Geometry-Guided Sampling Operator for Volumetric Segmentation

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
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arXiv AI
Jun 2

VDSB-GWSyn: Diffusion Schr\"{o}dinger Bridge for Controllable and Anatomically Feasible Guidewire Synthesis in Coronary Angiography

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
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arXiv Computer Vision
Oct 8

PPCAR-Net: Projection-Refined Parametric 3D Coronary Artery Reconstruction from Sparse X-ray Angiographic Views

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
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