arXiv AI

Harmonic-Aware Transformer for Real-Time Catheter Localization in Interventional Procedures of Magnetic Particle Imaging

arXiv:2607. 02919v1 Announce Type: cross Abstract: Magnetic particle imaging (MPI) enables real-time, radiation-free tracking of magnetic nanoparticle-coated instruments, making it highly suitable for interventional procedures.

arXiv Computer Vision
Sep 18

RAUL: Reference-Assisted Ureteroscopy Localization for Skill Assessment

RAUL is a reference‑assisted reconstruction framework that recovers ureteroscope trajectories from endoscopic video alone, using a high‑quality reference exploration video for each phantom. It achieves a mean translation error of 0.5 mm and increases frame‑wise localization coverage from 50.5 % to 86.1 % compared to standard Structure‑from‑Motion. The reconstructed trajectories reveal significant differences in navigation metrics between high‑ and low‑experience trainees, enabling objective skill assessment without external tracking equipment.

By Fangjie Li, Mai Bui, Charan Mohan, Michael Miga, Matthieu Chabanas, Nicholas Kavoussi, Jie Ying Wu
arXiv AI
Aug 11

Enhanced Real-Time 6-DOF Extended Reality Catheter Tracking for Evaluating Potential Improvement in Efficiency, Precision, and Depth Perception for Cardiac Interventions

arXiv:2608. 07606v1 Announce Type: cross Abstract: Despite advances in 3D ultrasound, most percutaneous cardiac interventions still rely on 2D visualization, limiting depth perception and spatial understanding.

By Mohsen Annabestani, Sandhya Sriram, Andrew Kuzemczak, S. Chiu Wong, Alexandros Sigaras, Bobak Mosadegh
arXiv Computer Vision
Aug 21

FermatSyn: SAM2-Enhanced Bidirectional Mamba with Isotropic Spiral Scanning for Multi-Modal Medical Image Synthesis

arXiv:2505. 07687v4 Announce Type: replace-cross Abstract: Multi-modal medical image synthesis is pivotal for alleviating clinical data scarcity, yet existing methods fail to reconcile global anatomical consistency with high-fidelity local detail.

By Feng Yuan, Yifan Gao, Haoyue Li, Xin Gao
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
arXiv AI
Jul 15

BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring

arXiv:2607. 11949v1 Announce Type: cross Abstract: We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware Transformer with Region-Aware Mamba (BAT-RM), a hybrid architecture that integrates Sobel-gated boundary attention, a linear-time, multi-directional Mamba module for long-range context, and a boundary-skeleton-guided fusion gate.

By Istiak Ahmed, Kazi Shahriar Sanjid, Galib Ahmed, Md. Tanzim Hossain, Md. Anwarul Islam, Shahrukh Khan, Md. Ashrif Rahman Arian, Md. Nishan Khan, Md. Misbah Khan, S M Hasibul Hoque, Rahnuma Shahrin Rista, Md. Jobairul Islam, Sheikh Anisul Haque, Md Arifur Rahman, Syed Md. Akram Hussain, Syeda Nashra, Sayeed Shafayet Chowdhury, Md. Mostafa Kamal Sarker, M. Monir Uddin
arXiv Machine Learning
Jul 10

Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)

arXiv:2604. 06482v2 Announce Type: replace-cross Abstract: Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiotherapy.

By Jiacheng Xie, Hua-Chieh Shao, Can Wu, Ricardo Otazo, Jie Deng, Mu-Han Lin, Tsuicheng Chiu, Jacob Buatti, Viktor Iakovenko, You Zhang
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
arXiv Machine Learning
Sep 21

Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation

The paper presents a Deep Operator Network that reconstructs full‑field 4D mitral regurgitation hemodynamics from sparse planar velocity data and boundary pressure traces. The network is pretrained on a URANS database of eleven orifice phantoms and then fine‑tuned on new cases, achieving rapid predictions in minutes. While the adaptation improves flow topology in the observed plane, reconstruction error increases sharply with distance from that plane, limiting physical consistency in the surrounding volume.

By Jakob Marcel Hoffmann, Yosuke Hasegawa, Alexander Stroh
arXiv AI
Sep 1

Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images

arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...

By Jakob Wasserthal, Joshy Cyriac, Michael Bach, Kimia Mozahheb Yousefi, Minh-Son To, M\'at\'e Sik, C\'edric H\'emon, Thomas Weikert, Martin Segeroth
arXiv Computer Vision
Sep 7

BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction

BEAM3R is a dose estimation framework that operates in beam’s-eye-view (BEV) for both photon and proton beamlet calculations. It uses a Mamba‑3 state‑space depth‑sequence core combined with physics‑based transport conditioning, sharing a 2D CNN encoder‑decoder architecture to process per‑plane BEV slices. The system achieves high accuracy on the DoseRAD2026 challenge, with local gamma pass rates above 96% for CT‑based models and runtimes under 25 s for photon and 18 s for proton predictions.

By Chen Cheng, Michael Ferraro, James Grover, David E J Waddington, Emily Hewson