Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, such DNN methods focus on applications involving test data that match the statistical characteristics of the training data very closely and give little attention to evaluating the performance of these DNNs on new out-of-distribution (OOD) acquisitions. We propose a novel DNN formulation that models the (i) underlying sinogram-based physics of the PET imaging system and (ii) the uncertainty in the DNN output through the per-voxel heteroscedasticity of the residuals between the predicted and the high-quality reference images. Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. Results on in vivo simultaneous PET-MRI, and various forms of OOD data in PET-MRI, show the benefits of suDNN over the current state of the art, quantitatively and qualitatively.
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）
Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction
Nanchang University（南昌大学）；Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（安徽省生物医学成像与智能处理重点实验室，人工智能研究院，合肥综合性国家科学中心）
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
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）
Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction
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）；Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司研发部）
Supervise-assisted Multi-modality Fusion Diffusion Model for PET Restoration
Beijing Institute of Technology（北京理工大学）；Research and Development Center of Agricultural Bank of China（中国农业银行研发中心）；Peking University（北京大学）；Beijing Normal University（北京师范大学）
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）
Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction
Sichuan University（四川大学）；University of Sydney（悉尼大学）；Chengdu University of Information Technology（成都信息工程大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）
针对低剂量 PET 重建中扩散模型采样慢、条件对应保持不足的问题，提出由 CPM 与 IRM 构成的由粗到细框架，并引入辅助引导和对比扩散策略，提升速度、重建质量及临床可靠性。
Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness
School of Computer Science, Sichuan University（四川大学计算机学院）；School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（上海科技大学生物医学工程学院 & 先进医学材料与器件国家重点实验室）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
Zhejiang University（浙江大学）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；Hangzhou Universal Medical Imaging Diagnostic Center（杭州全景医学影像诊断中心）
18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic results, which raises concerns about the potential health risks of radiation exposure, especially for pediatric patients. Reconstructing the low-dose PET (L-PET) images to the high-quality full-dose PET (F-PET) ones is an effective way that both reduces the radiation exposure and remains diagnostic accuracy. In this paper, we propose a resource-efficient deep learning framework for L-PET reconstruction and analysis, referred to as transGAN-S…
Gating is commonly used in PET imaging to reduce respiratory motion blurring and facilitate more sophisticated motion correction methods. In the applications of low dose PET, however, reducing injection dose causes increased noise and reduces signal-to-noise ratio (SNR), subsequently corrupting the motion estimation/correction steps, causing inferior image quality. To tackle these issues, we first propose a Siamese adversarial network (SAN) that can efficiently recover high dose gated image volume from low dose gated image volume. To ensure the appearance consistency between the recovered gate…
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
School of Computer Science, Sichuan University（四川大学计算机学院）；The Hong Kong Polytechnic University（香港理工大学）；School of Computer Science, Chengdu University of Information Technology（成都信息工程大学计算机学院）
Longitudinal NSCLC Treatment Progression via Multimodal Generative Models
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Italy（人工智能与计算机系统单位，工程系，罗马大学生物医学学院）；Multi-Specialist Clinical Institute for Orthopaedic Trauma Care (COT), Messina, Italy（骨科创伤护理多学科临床研究所（COT），意大利Messina）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden（诊断与介入系，放射物理，生物医学工程，乌梅大学，瑞典）；Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy（放射肿瘤手术研究单位，大学生物医学学院基金会，罗马，意大利）；Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Italy（放射肿瘤研究单位，医学与外科系，罗马大学生物医学学院，意大利）；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（生物医学工程系，巴塞尔大学，瑞士Allschwil）；Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy（海军、电气、电子与电信工程系，热那亚大学，意大利）
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。
Positron Emission Tomography (PET) is an important tool for studying Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to provide molecular characterization of patients with cognitive disorders. However, multiple tracers are needed to measure glucose metabolism (18F-FDG), synaptic vesicle protein (11C-UCB-J), and $β$-amyloid (11C-PiB). Administering multiple tracers to patient will lead to high radiation dose and cost. In addition, access to PET scans using new or less-available tracers with sophisticated production methods and short half-life isotopes may be very limit…
Northeastern University（东北大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance. In this paper, we propose a parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising. The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural…
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time consuming and associated with considerable radiation exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast whole-body PET reconstruction has the potential for cancer imaging with unprecedented speed and improved safety, but it cannot be achieved by the naive use of machine learning techniques. In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonan…
Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（J. Crayton Pruitt家族生物医学工程系，佛罗里达大学）；Department of Radiology, University of Florida（放射学系，佛罗里达大学）；Interdisciplinary Program in Bioengineering, Seoul National University（生物工程跨学科项目，首尔国立大学）；Department of Nuclear Medicine, Seoul National University College of Medicine（核医学系，首尔国立大学医学院）；Department of Radiology, University of California Davis Health（放射学系，加州大学戴维斯医学中心）
Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；Affiliated Hospital of Xiangnan University（湘南大学附属医院）；Jiangxi Cancer Hospital（江西省肿瘤医院）；Chenzhou Third People's Hospital（郴州第三人民医院）
ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain
School of Information Engineering, Nanchang University（南昌大学信息工程学院）；School of Mathematics and Computer Science, Nanchang University（南昌大学数学与计算机学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；College of Optical Science and Engineering, Zhejiang University（浙江大学光电科学与工程学院）
Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRI
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University（北卡罗来纳大学教堂山分校与北卡罗来纳州立大学生物医学工程联合系）；Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属精神卫生中心）
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）
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS). A reliable measure of the tissue myelin content is therefore essential for the understanding of the physiopathology of MS, tracking progression and assessing treatment efficacy. Positron emission tomography (PET) with $[^{11} \mbox{C}] \mbox{PIB}$ has been proposed as a promising biomarker for measuring myelin content changes in-vivo in MS. However, PET imaging is expensive and invasive due to the injection of a radioactive tracer. On the contrary, magnetic resonance imaging (MRI) is a non-invasive, widely …
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）
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection with PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, there is not an accurate automated segmentation method. Segmentation tends to be done manually by different imaging experts and it is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT…
POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Yale University（耶鲁大学）；University of California Irvine（加利福尼亚大学尔湾分校）；Canon Medical Research USA Inc.（佳能医疗研究美国公司）；Siemens Medical Solutions Inc.（西门子医疗解决方案公司）
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Duke University Medical Center（杜克大学医学中心）
This paper develops a method of biologically guided deep learning for post-radiation FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation F…
Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks
School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Zhongshan Hospital（中山医院）；State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（上海科技大学先进医用材料与器件国家重点实验室）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
The University of Tennessee（田纳西大学）；Siemens Medical Solutions Inc.（西门子医疗解决方案公司）
Direct reconstruction of positron emission tomography (PET) data using deep neural networks is a growing field of research. Initial results are promising, but often the networks are complex, memory utilization inefficient, produce relatively small 2D image slices (e.g., 128x128), and low count rate reconstructions are of varying quality. This paper proposes FastPET, a novel direct reconstruction convolutional neural network that is architecturally simple, memory space efficient, works for non-trivial 3D image volumes and is capable of processing a wide spectrum of PET data including low-dose a…
VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH（VRVis虚拟现实与可视化研究有限公司）；Center for Medical Physics and Biomedical Engineering, Medical University of Vienna（维也纳医科大学医学物理与生物医学工程中心）；Center for Nuclear Medicine, Clinical Center of Serbia（塞尔维亚临床中心核医学中心）
Tumor segmentation in multimodal medical images has seen a growing trend towards deep learning based methods. Typically, studies dealing with this topic fuse multimodal image data to improve the tumor segmentation contour for a single imaging modality. However, they do not take into account that tumor characteristics are emphasized differently by each modality, which affects the tumor delineation. Thus, the tumor segmentation is modality- and task-dependent. This is especially the case for soft tissue sarcomas, where, due to necrotic tumor tissue, the segmentation differs vastly. Closing this …
