LDCT Denoising

LDCT: Low-Dose Computed Tomography

LDCT denoising studies how to suppress noise caused by reduced radiation exposure without obscuring anatomy or lesions. Approaches include CNNs and transformers, diffusion and autoregressive models, and physics-informed methods that learn from projection data; self-supervised strategies aim to work when paired clean scans are unavailable. A central challenge is generalizing across dose levels, scanners, and anatomical regions, while balancing efficient inference with realistic detail recovery. Standard metrics such as PSNR and SSIM can reward smoother images even when lesion detectability suffers, so clinical and task-based evaluation remains an important open issue.

Latest papers

20 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
Oct 13, 2026Langrui Zhou, Guang Li, Xinyu Huang +5Self-Supervised Image Denoising LDCT Denoising
Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210009, China. · Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 201203, China · Jiangsu Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Institute of Clinical Medicine, Nanjing Drum Tower Hospital, Medical School, Nanjing University, Nanjing 210093, China
RIQE: a NIQE-style reference model for Computed Tomography
Sep 30, 2026Fabio MattiussiImage Quality Assessment Computed Tomography
EOC
One-Step Is Optimal: Unconditional Rectified Flows are Noise2Noise Denoisers, and Multi-Step Integration Provably Hurts---A Benchmark and Task-Based Detectability Study on Low-Dose CT
Sep 15, 2026Timothy Sereda, Debesh JhaSelf-Supervised Image Denoising Rectified Flow
Department of Computer Science Biomedical Perception & Intelligence Lab University of South Dakota Vermillion, South Dakota, USA
A deep dictionary network-based foundation model for ultra-low-dose CT denoising
Sep 11, 2026Baoshun Shi, Shuangyi Yang, Ke Jiang +3LDCT Denoising Medical Imaging Foundation Models
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China · Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, Hebei, China · Department of Orthopedics, Beijing Friendship Hospital, Capital Medical University, Beijing, China +2
Physics-Driven Independent Pair Generation for Iterative Self-Supervised Low-Dose CT Denoising
Sep 1, 2026Xianlei Han, Shaoyu Wang, Jiancheng Fang +2Self-Supervised Image Denoising Self-Supervised Learning
School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China · School of Information Engineering, Nanchang University, Nanchang 330031, China · School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 510275, Guangdong, China
Efficient Noise Context-Aware Representation for Low-Dose CT Denoising
Aug 11, 2026Minwoo Yu, N. Robert Bennett, Jongduk Baek +1Dynamic Convolution Computed Tomography
Department of Artificial Intelligence, Yonsei University, South Korea · Department of Radiology, Stanford University, Stanford, CA 94305 USA · Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA
GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization
Jul 11, 2026Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor +3Domain Generalization Image Reconstruction
College of Computer Science, Chongqing University, Chongqing, China · Department of Software Engineering, Daffodil International University, Bangladesh · Faculty of Information Science & Technology, Multimedia University, Malaysia
ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising
Jul 2, 2026Xuepeng Liu, Zetong Liu, Renyiming Li +5Diffusion Models for Image Restoration Diffusion Model Guidance
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China · State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510060, China · AI Lab, Tohoku University Hospital, Sendai, Miyagi, Japan +1
DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling
Jun 26, 2026Xizhuo Zhang, Yannian Gu, Zhongzhen Huang +2Visual Autoregressive Models Computed Tomography
Shanghai Jiao Tong University, China · SenseTime Research, China · Shanghai Innovation Institute, China
FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoising
May 11, 2026Guoquan Wei, Liu Shi, Chong Chen +1Self-Supervised Image Denoising Self-Supervised Learning
School of Information Engineering, Nanchang University, Nanchang 330031, China · SKLMS, ICMSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT
May 1, 2026Johannes B. Thalhammer, Lorenzo D'Amico, Lucy Costello +11Self-Supervised Image Denoising Computed Tomography
Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, TU Munich · Munich Institute of Biomedical Engineering, TU Munich · Institute for Diagnostic and Interventional Radiology, TUM Klinikum +3
Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks
May 1, 2026Zhilin Guan, Wei ZhangComputed Tomography LDCT Denoising
Department of Computing Harbin Institute of Technology Harbin, China
TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising
Mar 21, 2026Tangtangfang Fang, Yang Jiao, Xiangjian He +2LDCT Denoising Diffusion Models for Image Restoration
University of Nottingham Ningbo China, Ningbo, Zhejiang, China
One CT Unified Model Training Framework to Rule All Scanning Protocols
Mar 16, 2026Fengzhi Xu, Ziyuan Yang, Zexin Lu +4Computed Tomography Image Reconstruction
Sichuan University, Chengdu, China · City University of Hong Kong, Hong Kong, China · Fudan University, Shanghai, China
Projection Guided Personalized Federated Learning for Low Dose CT Denoising
Mar 12, 2026Anas Zafar, Muhammad Waqas, Amgad Muneer +2LDCT Denoising Federated Learning
The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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
PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging
Feb 25, 2026Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn +1LDCT Denoising Image Denoising
Aston Institute of Photonic Technologies, Aston University, Aston Triangle, Birmingham, B4 7ET, West Midlands, United Kingdom
A Green Learning Approach to LDCT Image Restoration
Feb 23, 2026Wei Wang, Yixing Wu, C. -C. Jay KuoImage Restoration LDCT Denoising
University of Southern California, Los Angeles, California, USA
IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising
Feb 22, 2026Guoliang Gong, Man YuLDCT Denoising Image Denoising
Tianjin University of Science and Technology
FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
Aug 24, 2025Zhihao Chen, Qi Gao, Zilong Li +4Diffusion Models for Image Restoration Medical Imaging Foundation Models
Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China · Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China · School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China +1

Oct 13, 2026J/K move · Enter open · S save

Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching

Langrui Zhou, Guang Li, Xinyu Huang, Ziteng Zhou+4

Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210009, China. · Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 201203, China · Jiangsu Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Institute of Clinical Medicine, Nanjing Drum Tower Hospital, Medical School, Nanjing University, Nanjing 210093, China

Self-Supervised Image Denoising LDCT Denoising

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With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed tomography (CT) denoising. However, they require paired noisy-clean training data, which is difficult to obtain in practice. Some classic self-supervised denoising methods rely on the assumption of pixel-wise independent noise, making them ineffective for correlated noise in low-dose CT (LDCT) images. Considering the high similarity and weaker noise correlation between adjacent CT slices, several recently proposed self-supervised methods use adjacent LDCT slices for training. Nevertheless, these approaches fail to effectively account for the non-rigid misalignment between slices caused by anatomical variation, hindering them from satisfying the equal-mean condition required by the classic Noise2Noise framework. In this paper, we propose Noise2Match, a self-supervised LDCT denoising method based on adaptive inter-slice matching. Noise2Match introduces an inter-slice matching network to model the label transition process between adjacent slices, and then applies correction to the denoiser's outputs to better satisfy the equal-mean condition. We theoretically prove that, under idealized conditions, training Noise2Match using adjacent LDCT slices is equivalent to supervised training with paired noisy-clean images. We further find that Noise2Match can also effectively perform low-dose conebeam CT (CBCT) denoising, which could be attributed to its correction operation that reduces inter-slice noise correlation. To the best of our knowledge, Noise2Match is the first effective image-domain self-supervised method for low-dose CBCT. Extensive experiments demonstrate that Noise2Match outperforms existing image-domain selfsupervised methods, offering broader applicability and higher practical value.