Accurate quantification and uptake measurement in PET are critical for assessing disease progression and supporting clinical decision-making. While high-count PET provides reliable image quality, the associated radiation dose and prolonged acquisition remain significant clinical concerns, motivating the adoption of low-count protocols. Diffusion-model-based methods have demonstrated strong potential for restoring low-count PET to near high-count quality, but their iterative sampling procedure becomes prohibitively expensive when applied to high-resolution 3D PET volumes, introducing substantial inference latency that limits practical clinical deployment. To address these challenges, we propose a training-free Global-Local Skipping Strategy that accelerates diffusion model-based 3D PET denoising while simultaneously improving reconstruction quality. The proposed method is plug-and-play and directly applicable to pre-trained diffusion models without retraining or architectural modification. Specifically, we introduce: (i) a global denoising step skipping strategy that initializes the reverse diffusion process from an intermediate denoising step using a noise-consistent transformation of the low-count input, substantially reducing the number of required denoising steps; and (ii) a local feature reuse shortcut that reuses slowly-varying high-level U-Net features across neighboring denoising steps, further reducing per-step computation while preserving image fidelity. We evaluate the proposed approach on multiple PET tracers from in-house and public datasets, including 18F-FDG PET, 68Ga-DOTATATE PET, and 18F-PSMA PET, demonstrating consistent acceleration of over an order of magnitude alongside improved or comparable reconstruction performance relative to the full-step baseline. Blinded reader studies further confirm enhanced clinical confidence and perceived diagnostic quality.
AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Tohoku University School of Medicine（东北大学医学院）；Department of Clinical Neurosciences, University of Cambridge（剑桥大学临床神经科学系）
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Neuroscience Research Institute（神经科学研究所）；Gachon University of Medicine and Science（嘉泉医科大学）
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising
School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南方医科大学南芳医院PET中心）
DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁医学院）；Siemens Medical Solutions USA, Inc.（西门子医疗解决方案美国公司）；Inselspital, Bern University Hospital（伯尔尼大学医院Inselspital）；University of Bern（伯尔尼大学）；Department of Biomedical Engineering（生物医学工程系）；Department of Radiology and Biomedical Imaging（放射科和生物医学成像系）；Department of Medicine (Cardiology)（医学系（心脏病学））
MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising
School of Engineering, Zurich University of Applied Sciences, CH；Bioengineering Department；Imperial-X, Imperial College London, UK；DAMTP, University of Cambridge, UK；Lucerne University Teaching；Research Hospital, CH；Lung Institute, Imperial College London, UK；Cardiovascular Research Centre, Royal Brompton Hospital, UK；School of Biomedical Engineering \& Imaging Sciences, King's College London, UK；Yau Mathematical Sciences Center, Tsinghua University, CN
3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising
School of Engineering, Zurich University of Applied Sciences, CH；Bioengineering Department；Imperial-X, Imperial College London, UK；DAMTP, University of Cambridge, UK；Lucerne University Teaching；Research Hospital, CH；Lung Institute, Imperial College London, UK；Cardiovascular Research Centre, Royal Brompton Hospital, UK；School of Biomedical Engineering \& Imaging Sciences, King's College London, UK；Yau Mathematical Sciences Center, Tsinghua University, CN
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
Yale University（耶鲁大学）；Northwestern University（西北大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Inselspital, Bern University Hospital, University of Bern（伯尔尼大学医院 Inselspital，伯尔尼大学）；Technical University of Munich（慕尼黑工业大学）；Yale School of Medicine（耶鲁医学院）
Generating healthy counterfactuals with denoising diffusion bridge models
Athinoula A. Martinos Center for Biomedical Imaging（阿提诺拉A·马丁诺生物医学成像中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；ETH Zurich（苏黎世联邦理工学院）；Computer Science & Artificial Intelligence Lab（计算机科学与人工智能实验室）；Hawkes Institute（哈维斯研究所）；University College London（伦敦大学学院）
FlowPET: Physics-Informed Symplectic Flow Matching for Low-Count PET Reconstruction
Centre for Smart Health, The Hong Kong Polytechnic University, Hong Kong, China（智能健康研究中心，香港理工大学，中国香港）；Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China（核医学部，中山大学肿瘤中心，中国广州）；Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（医学人工智能研究中心，深圳先进技术研究院，中国科学院，中国深圳）
UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
School of Biological Science and Medical Engineering（生物科学与医学工程学院）；State Key Laboratory of Software Development Environment（软件开发环境国家重点实验室）；Key Laboratory of Biomechanics, Mechanobiology of Ministry of Education（教育部生物力学与机械生物学重点实验室）；Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University（北京生物医学工程先进创新中心，北航）；Department of Biomedical Engineering（生物医学工程系）；Department of Gynecology Oncology（妇科肿瘤科）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital（国家癌症中心/国家癌症临床研究中心/癌症医院）；Chinese Academy of Medical Sciences（中国医学科学院）；Peking Union Medical College（北京协和医学院）；Xiaomi Corporation（小米公司）
Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning
IWR, Heidelberg University（海德堡大学IWR）；Silicon Austria Labs（Silicon Austria实验室）；College of Medicine and Biological Information Engineering, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University（医学与生物信息工程学院，医学图像智能计算教育部重点实验室，东北大学）；Department of Epidemiology & Global Health, Umeå University（流行病学与全球健康系，乌梅大学）
End-to-End PET Image Reconstruction via a Posterior-Mean Diffusion Model
Department of Electrical and Computer Engineering, Rice University, Houston, USA（电气与计算机工程系，里士大学，美国休斯顿）；Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, USA（影像物理系，德克萨斯大学MD安德森癌症中心，美国休斯顿）
PET image denoising based on denoising diffusion probabilistic models
Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院戈登医学成像中心）；Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院高级医学计算与分析中心）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study
organization= Department of Physics \& Astronomy, University of British Columbia , city= Vancouver , state= BC , country= Canada；organization= Department of Integrative Oncology, BC Cancer Research Institute , city= Vancouver , state= BC , country= Canada；organization= Department of Radiology, University of British Columbia , city= Vancouver , state= BC , country= Canada
U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training
School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院）；Department of Biomedical Engineering, Tsinghua University（清华大学生物医学工程系）；School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）；ByteDance Inc.（字节跳动有限公司）
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models
BC Cancer Research Institute（BC癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；BC Cancer（BC癌症中心）
2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction
Yale School of Medicine（耶鲁医学院）；University of California Irvine（加州大学欧文分校）；Yale University（耶鲁大学）
A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Models
Department of Computer Science and Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校计算机科学与工程系）；Mallinckrodt Institute of Radiology, Washington University in St. Louis（华盛顿大学圣路易斯分校马林克罗德特放射医学研究所）；Department of Electrical and Systems Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校电气与系统工程系）；Department of Neurology, Washington University in St. Louis（华盛顿大学圣路易斯分校神经病学系）；Department of Biomedical Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校生物医学工程系）；Division of Biology and Biomedical Sciences, Washington University in St. Louis（华盛顿大学圣路易斯分校生物学与生物医学科学 division）；Department of Electrical and Computer Engineering, University of Wisconsin–Madison（威斯康星大学麦迪逊分校电气与计算机工程系）
A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising
Zhejiang Polytechnic University of Mechanical and Electrical Engineering（浙江机电职业技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥国家综合科学中心人工智能研究院）；School of Electrical and Optical Engineering（南京理工大学电气与光学工程学院）；Zhejiang Lab（浙江实验室）；School of Intelligent Science and Technology（智能科学与技术学院）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau（澳门大学生物医学成像实验室）；College of Optical Science and Engineering（光学工程学院）；College of Biomedical Engineering and Instrument Science（生物医学工程与仪器科学学院）
End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
提出端到端三域 TriPLET 框架，融合投影域去噪、小波域重建和图像域对抗评估，从低剂量 PET 正弦图恢复标准剂量图像，并取得最优相似度与信噪比。
CT-Less Attenuation Correction Using Multiview Ensemble Conditional Diffusion Model on High-Resolution Uncorrected PET Images
Department of Medical Imaging and Radiation Sciences（医学影像与放射科学系）；Université de Sherbrooke（Sherbrooke大学）；Sherbrooke Molecular Imaging Center（Sherbrooke分子影像中心）；CRCHUS；Nantes Université（Nantes大学）；Laboratoire CRCI2NA, INSERM, CNRS（CRCI2NA实验室，INSERM，CNRS）；Imaging Research and Technology (IR&T) Inc.（影像研究与技术公司）；Research Center on Aging, Department of Medicine（衰老研究中心，医学系）；Department of Electrical Engineering and Computer Engineering（电气工程与计算机工程系）
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
Yale University（耶鲁大学）；University of Southern California（南加州大学）；University of California Irvine（加州大学欧文分校）；University of Science and Technology of China（中国科学技术大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；University of Bern（伯尔尼大学）；Technical University of Munich（慕尼黑工业大学）
Exploiting network optimization stability for enhanced PET image denoising using deep image prior
Central Research Laboratory, Hamamatsu Photonics K. K.（滨松光子学株式会社中央研究所）；Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST)（国立量子科学技术研究开发机构量子医学科学研究所）；J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（佛罗里达大学J. Crayton Pruitt家族生物医学工程系）
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
School of Biomedical Engineering, Southern Medical University（生物医学工程学院，南方医科大学）；School of Biomedical Engineering, Shanghai Jiao Tong University（生物医学工程学院，上海交通大学）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南芳PET中心，南方医科大学南芳医院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University（广东省医学图像处理重点实验室，南方医科大学）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University（广东省医学影像与诊断技术工程实验室，南方医科大学）；Pazhou Lab（琶洲实验室）
A Noise-level-aware Framework for PET Image Denoising
Center of Advanced Medical Computing and Analysis, MGH/HMS（麻省总医院/哈佛医学院高级医学计算与分析中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；GE Healthcare（通用电气医疗）；University of Bern（伯尔尼大学）
在PET中，不同身体区域中存在的相对（信号依赖性）噪声量可能显著不同，并且与该区域中存在的计数数量固有相关。在原则上和其他因素中，区域中的计数的数量取决于总的施用活动、扫描仪灵敏度、图像采集持续时间、区域中的放射性药物示踪剂摄取和区域周围的患者局部身体形态测量。理论上，对高计数（低相对噪声）图像进行去噪所需的去噪操作量少于对低计数（高相对噪声）图像进行去噪所需的去噪操作量，反之亦然。当前基于深度学习的PET图像去噪方法主要仅在图像外观上进行训练，对不同噪声水平的图像没有特殊处理。我们的假设是，通过将输入图像的局部相对噪声水平显式地提供给深度卷积神经网络（DCNN），DCNN可以超越仅在图像外观上训练的自身。为此，我们提出了一个噪声水平感知框架去噪框架，允许将局部噪声水平嵌入到DCNN中。在GE Discovery MI PET/CT系统上采集的30和15例患者PET图像上对申报器械进行了训练和测试。我们的实验表明，我们的主干网与相对噪声水平嵌入（NLE）相比，PSNR和SSIM的增加在统计学上是显著的，p<0.001，所提出的方法大大优于强基线方法。
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
Gordon Center for Medical Imaging（戈登医学影像中心）；Center for Advanced Medical Computing and Analysis（高级医学计算与分析中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Florida（佛罗里达大学）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Johns Hopkins University（约翰斯·霍普金斯大学）
针对 PET 图像噪声及三维全局自注意力计算成本高的问题，提出融合局部与全局 MSA 的空间通道编解码 Transformer，并在多示踪剂数据集上验证其去噪性能优于现有深度学习架构。
Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior
Gordon Center for Medical Imaging（戈登医学影像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Martinos Center for Biomedical Imaging（马蒂诺斯生物医学影像中心）；University of California, Davis（加州大学戴维斯分校）
PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
School of Computer Science and Technology, Guangdong University of Technology（广东技术大学计算机科学与技术学院）；Zhongshan School of Medicine, Sun Yat-sen University（中山大学仲沙医学院）；School of Mathematics and Statistics, Guangdong University of Technology（广东技术大学数学与统计学院）；School of Mathematics (Zhuhai), Sun Yat-sen University（中山大学珠海数学学院）；Future Technology Institute, South China University of Technology（华南理工大学未来技术研究院）；School of Computer Science and Engineering, Huizhou University（惠州大学计算机科学与工程学院）
Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA（佛罗里达大学电气与计算机工程系）；Department of Medicine, University of Florida, Gainesville, FL, USA（佛罗里达大学医学系）；Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA（佛罗里达大学生物医学工程系）；Research Computing, University of Florida, Gainesville, FL, USA（佛罗里达大学研究计算中心）；NVIDIA, Santa Clara, CA, USA（英伟达）
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms
School of Computer Science, Sichuan University（四川大学计算机学院）；School of Computer Science, Chengdu University of Information Technology（成都信息工程大学计算机学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models
Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；Digital Surgery LAB（数字外科实验室）；Champalimaud Foundation（尚帕利莫基金会）；Faculdade de Medicina, Universidade de Lisboa（里斯本大学医学院）；Instituto de Telecomunicações（电信研究所）
