Cone-Beam CT

CT: Computed Tomography

Cone-beam computed tomography studies how to form useful 3D images from X-ray projections, often with limited views, noise, motion, metal artifacts, or an incomplete field of view. Reconstruction methods range from filtered back-projection and iterative, physics-based optimization to learned CNNs, diffusion models, implicit neural representations, and Gaussian splatting that combine image priors with projection consistency. Related work improves image quality and supports tasks such as segmentation, CBCT-to-CT synthesis, motion-resolved imaging, registration, and report generation. A central challenge is balancing artifact reduction and speed against faithful preservation of patient anatomy, with clinical task-based evaluation needed alongside image-similarity metrics.

Latest papers

47 papers7 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis
May 19, 2026Alzahra Altalib, Chunhui Li, Haytham Ahmad Alewaidat +3Physics-Informed Diffusion Models Cone-Beam CT
School of Science and Engineering, University of Dundee UK · Faculty of Applied Medical Sciences, Jordan University of Science and Technology · Experia Healthcare, Jordan +1
Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
May 13, 2026Viktor Rogowski, Maarten L. Terpstra, Niklas Wahl +30Cone-Beam CT Medical Image Synthesis
Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden · Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden · Radiotherapy Department, University Medical Center Utrecht, Utrecht, The Netherlands +20
Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis
May 3, 2026Xinquan Yang, Jianfeng Ren, Xuguang Li +4Cone-Beam CT Self-Supervised Pre-Training
School of Artiﬁcial Intelligence, Shenzhen University, Shenzhen, China. · School of Computer Science, University of Notting-ham Ningbo China, Ningbo, China. · Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China.
Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstruction
Apr 30, 2026Jian Lin, Jiancheng Fang, Shaoyu Wang +43D Gaussian Splatting Cone-Beam CT
School of Information Engineering, Nanchang University, Nanchang 330031, China · Laboratory of Image Science and Technology, the School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University, Nanjing 210096, China
Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
Apr 23, 2026Luis Barba, Johannes Kirschner, Benjamin BejarCone-Beam CT 3D Medical Imaging
Swiss Data Science Center (SDSC) in Paul Scherrer Institute (PSI), Villigen, Switzerland. · Swiss Data Science Center (SDSC) and ETH Zurich, Switzerland. · Swiss Data Science Center (SDSC) and Paul Scherrer Institute (PSI), Villigen, Switzerland.
No-reference based automatic parameter optimization for iterative reconstruction using a novel search space aware crow search algorithm
Apr 6, 2026Poorya MohammadiNasab, Ander Biguri, Philipp Steininger +9Hyperparameter Optimization Image Reconstruction
Department of Medicine, Danube Private University (DPU), Krems, Austria · Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom +3
HICT: High-precision 3D CBCT reconstruction from a single X-ray
Apr 1, 2026Wen Ma, Jiaxiang Liu, Zikai Xiao +3Dental Imaging Medical Imaging
Zhejiang University · Angelalign Technology Inc.
Cone-Beam CT Image Quality Enhancement Using A Latent Diffusion Model Trained with Simulated CBCT Artifacts
Mar 27, 2026Naruki Murahashi, Mitsuhiro Nakamura, Megumi NakaoCone-Beam CT Diffusion Models for Image Restoration
Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, JAPAN
ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction
Mar 16, 2026Seungryong Lee, Woojeong Baek, Joosang Lee +1Cone-Beam CT Image Reconstruction
Sungkyunkwan University · Yonsei University
SemiTooth: a Generalizable Semi-supervised Framework for Multi-Source Tooth Segmentation
Mar 12, 2026Muyi Sun, Yifan Gao, Ziang Jia +4Medical Image Segmentation Semi-Supervised Medical Image Segmentation
School of AI, BUPT · CBDE · NLPR, CASIA +3
Bridging the Skill Gap in Clinical CBCT Interpretation with CBCTRepD
Mar 11, 2026Qinxin Wu, Fucheng Niu, Hengchuan Zhu +9Radiology Report Generation Human-AI Collaboration
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China. · College of Computer Science and Technology, Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Hangzhou 310027, Zhejiang, China.
Robotic Ultrasound Makes CBCT Alive
Mar 10, 2026Feng Li, Ziyuan Li, Zhongliang Jiang +2Cone-Beam CT Deformable Image Registration
Chair for Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · The University of Hong Kong, Hong Kong, China
Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy
Mar 10, 2026Gauthier Miralles, Loïc Le Folgoc, Vincent Jugnon +1Unsupervised Domain Adaptation Cross-Modality Medical Image Segmentation
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France · GE Healthcare, Buc, France
3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models
Mar 6, 2026Susanne Schaub, Florentin Bieder, Matheus L. Oliveira +4Cone-Beam CT Computed Tomography
Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland · Piracicaba Dental School, University of Campinas, Piracicaba, Brazil · State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China +2
RAFM: Retrieval-Augmented Flow Matching for Unpaired CBCT-to-CT Translation
Feb 28, 2026Xianhao Zhou, Jianghao Wu, Lanfeng Zhong +4Medical Image Synthesis Image-to-Image Translation
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China · Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia · Shanghai Artificial Intelligence Laboratory, Shanghai, China
HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography
Feb 26, 2026Khuram Naveed, Ruben PauwelsImage Denoising LDCT Denoising
Department of Dentistry and Oral Health, Aarhus University, Aarhus, 8000, Denmark
Artifact Reduction in Undersampled 3D Cone-Beam CTs using a Hybrid 2D-3D CNN Framework
Feb 9, 2026Johannes Thalhammer, Tina Dorosti, Sebastian Peterhansl +3Cone-Beam CT Sparse-View CT Reconstruction
Chair of Biomedical Physics, Department of Physics, School of Natural Sciences · Munich Institute of Biomedical Engineering · Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum +2

May 19, 2026J/K move · Enter open · S save

EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis

Alzahra Altalib, Chunhui Li, Haytham Ahmad Alewaidat, Khaled Z. Alawneh+2

School of Science and Engineering, University of Dundee UK · Faculty of Applied Medical Sciences, Jordan University of Science and Technology · Experia Healthcare, Jordan+1

Physics-Informed Diffusion Models Cone-Beam CT Latent Diffusion Models Physics-Informed ML Medical Image Synthesis Conditional Diffusion Models

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Cone-beam CT (CBCT) is routinely acquired during radiotherapy for patient setup, but its quantitative reliability is degraded by scatter, noise, and reconstruction artifacts, limiting Hounsfield Unit (HU) accuracy. We propose EPC-3D-Diff, a novel conditional 3D latent diffusion framework for volumetric CBCT to CT synthesis that introduces a projection domain equivariance loss derived from acquisition physics. Unlike common image domain equivariance, we exploit the fact that an in plane rotation of the volume corresponds to an angular shift in its projections. During training, we enforce this relationship by forward projecting rotated synthesized CT volumes and matching them to appropriately angle shifted projections of the paired target CT, yielding a physics consistent equivariance constraint integrated into the diffusion objective. To capture full 3D context efficiently, conditional diffusion is performed in a compact latent space learnt by a lightweight 3D autoencoder, preserving axial depth while downsampling in plane resolution for stable training. We validate on a paired head CBCT/CT phantom dataset, including repeat scans, and paired clinical data using patient wise splits, and perform single and mixed domain training, ablations, and comparisons with diffusion and CycleGAN. EPC-3D-Diff generalizes well and achieved substantial improvements, +7.4 dB (phantom) and +1.8 dB (clinical data) in PSNR compared to state of the art methods, alongside improved SSIM and HU accuracy, within tissue boundaries. Overall, EPC-3D-Diff improves robustness and physics consistency, supporting HU aware synthesis for downstream radiotherapy workflows. The open source code for EPC-3D-Diff is available at https://github.com/ALZAHRAALTALIB/EPC-3D-Diff.