Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization
University of Michigan-Shanghai Jiao Tong University Joint Institute（上海交通大学密西根学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术学院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
[$^{18}$F]fluorodeoxyglucose (FDG) positron emission tomography (PET) has emerged as a crucial tool in identifying the epileptic focus, especially in cases where magnetic resonance imaging (MRI) diagnosis yields indeterminate results. FDG PET can provide the metabolic information of glucose and help identify abnormal areas that are not easily found through MRI. However, the effectiveness of FDG PET-based assessment and diagnosis depends on the selection of a healthy control group. The healthy control group typically consists of healthy individuals similar to epilepsy patients in terms of age, gender, and other aspects for providing normal FDG PET data, which will be used as a reference for enhancing the accuracy and reliability of the epilepsy diagnosis. However, significant challenges arise when a healthy PET control group is unattainable. Yaakub \emph{et al.} have previously introduced a Pix2PixGAN-based method for MRI to PET translation. This method used paired MRI and FDG PET scans from healthy individuals for training, and produced pseudo normal FDG PET images from patient MRIs that are subsequently used for lesion detection. However, this approach requires a large amount of high-quality, paired MRI and PET images from healthy control subjects, which may not always be available. In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization. Two deep learning methods, CycleGAN and SynDiff, were employed, and we found that diffusion-based method achieved improved performance in accurately localizing the epileptic focus.
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）
Translating MRI to PET through Conditional Diffusion Models with Enhanced Pathology Awareness
Lab for Artificial Intelligence in Medical Imaging, Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)（人工智能医学成像实验室，诊断与介入放射学研究所，医学院与健康学院，TUM医院，慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Nuclear Medicine, School of Medicine and Health（核医学系，医学院与健康学院）；Department of Neuroradiology, School of Medicine and Health（神经放射学系，医学院与健康学院）
Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Basis Scottsdale（斯科茨代尔贝斯机构）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；Mayo Clinic Arizona（梅奥诊所亚利桑那州分院）
TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
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 & Biomedical Imaging, Yale University（放射学与生物医学成像系，耶鲁大学）；Department of Medicine, University of Florida（医学系，佛罗里达大学）；Banner Alzheimer’s Institute（Banner阿尔茨海默症研究所）
RelA-Diffusion: Relativistic Adversarial Diffusion for Multi-Tracer PET Synthesis from Multi-Sequence MRI
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill（放射科与生物医学研究成像中心（BRIC）、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（生物医学工程联合部门、北卡罗来纳大学教堂山分校和北卡罗来纳州立大学）；Department of Exercise and Sport Science, UNC-CH（运动与体育科学系、UNC-CH）
Automatic and Manual Segmentation of Hippocampus in Epileptic Patients MRI
Rutgers University（罗格斯大学）；Henry Ford Health System（亨利·福特医疗系统）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Spectrum Health System（光谱健康系统）；Michigan State University（密歇根州立大学）；University of Tehran（德黑兰大学）
MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging
Department of Biomedical Engineering, Wake Forest University School of Medicine（生物医学工程系，威克森林大学医学院）；Department of Radiology, Wake Forest School of Medicine（放射学系，威克森林医学院）；Department of Radiology and Biomedical Imaging, Yale School of Medicine（放射学与生物医学成像系，耶鲁医学院）
Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI
Dublin City University（都柏林城市大学）；Insight Research Ireland Centre for Data Analytics（爱尔兰Insight研究数据分析师中心）；St. Vincent’s University Hospital（圣文森医院）；University College Dublin（都柏林大学）
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）
Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model
Washington University in St. Louis（圣路易斯华盛顿大学）；Yale University（耶鲁大学）；Western University（西安大略大学）；Lawson Health Research Institute（劳森健康研究所）；McGill University（麦吉尔大学）；Siemens Healthineers AG（西门子医疗保健公司）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）
When Brains Disagree: Biological Ambiguity Underlies the Challenge of Amyloid PET Synthesis from Structural MRI
Hawkes Institute, University College London, UK（霍克斯研究所，伦敦大学学院，英国）；Department of Medical Physics and Biomedical Engineering, University College London, UK（医学物理与生物医学工程系，伦敦大学学院，英国）；AINOSTICS Ltd, Manchester, UK（AINOSTICS有限公司，曼彻斯特，英国）；Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, UK（痴呆研究中心，伦敦大学学院女王广场神经科学研究所，英国）；UK Dementia Research Institute, London, UK（英国痴呆研究研究所，伦敦，英国）；Department of Computer Science, University College London, UK（计算机科学系，伦敦大学学院，英国）
Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers
organization= Shanghai Artificial Intelligence Laboratory , city= Shanghai , postcode= 200082 , country= China；organization= School of Medicine, Xiamen University , city= Xiamen , state= Fujian , country= China；organization= Department of Nuclear Medicine \& PET Center, Huashan Hospital, Fudan University , city= Shanghai , country= China；organization= Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital , city= Shanghai , country= China；organization= Department of Computer Science, Rutgers University , city= New Brunswick , state= New Jersey , country= United States；organization= Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , city= Shenzhen , country= China
Diffusion Bridge Networks Simulate Clinical-grade PET from MRI for Dementia Diagnostics
Lab for AI in Medical Imaging, Institute for Diagnostic and Interventional Radiology, School of Medicine and Health（人工智能在医学影像中的实验室，诊断与介入放射学研究所，医学院与健康学院）；Technical University of Munich (TUM)（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University Medical Center Hamburg-Eppendorf（汉堡-埃本达大学医学中心）；Department of Neuroradiology, TUM University Hospital, School of Medicine and Health（神经放射学部，慕尼黑技术大学医院，医学院与健康学院）；Department of Neurology, TUM University Hospital, School of Medicine and Health（神经病学部，慕尼黑技术大学医院，医学院与健康学院）
Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model
Inner Mongolia University（内蒙古大学）；Inner Mongolia Medical University（内蒙古医科大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Inner Mongolia Medical University Affiliated Hospital（内蒙古医科大学附属医院）
针对 PET-MRI 系统中 MRI 采集耗时长且 PET 信噪比低的问题，本文提出一种基于扩散随机微分方程的联合重建模型，通过学习两者的联合概率分布加速 MRI 并提升 PET 质量，在定性和定量上超越现有最先进方法。
University of California, San Francisco（加州大学旧金山分校）；University of New Mexico（新墨西哥大学）；Washington University（华盛顿大学）；University of Iowa（爱荷华大学）；University of Washington（华盛顿大学）
Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge
University of Science, VNU-HCM（越南国立大学胡志明市理科大学）；University of New Brunswick（新不伦瑞克大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡）
Bridging MRI and PET physiology: Untangling complementarity through orthogonal representations
Dept. of diagnostic and interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany（德国奥格斯堡大学医院诊断与介入放射学及神经放射学系）；Digital Medicine, University Hospital Augsburg, Germany（德国奥格斯堡大学医院数字医学）；Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Germany（德国慕尼黑工业大学计算机辅助医疗流程与增强现实教席）；Bavarian Center for Cancer Research (BZKF) Augsburg, Germany（德国奥格斯堡巴伐利亚癌症研究中心）；Dept. of Nuclear Medicine, University Hospital Augsburg, Germany（德国奥格斯堡大学医院核医学系）；Dept. of Urology, University Hospital Augsburg, Germany（德国奥格斯堡大学医院泌尿外科）；Center for Advanced Analytics and Predictive Sciences, University of Augsburg, Germany（德国奥格斯堡大学高级分析与预测科学中心）
Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
School of Software Yunnan University（软件学院 云南大学）；Department of Computer Science The University of Sheffield（计算机科学系 剑桥大学）；Department of Computing Xian JiaoTong-Liverpool University（计算系 西交利物浦大学）
Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment
School of Mathematics and Statistics, Xi'an Jiaotong University（西安交通大学数学与统计学学院）；Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）；Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University（首都医科大学宣武医院放射科与核医学科）；Research Center for Intelligent Medical Equipment and Devices (IMED), Xi'an Jiaotong University（智能医疗设备与器件研究中心（IMED），西安交通大学）
Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data
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）
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China（生物医学工程系，香港理工大学，香港特别行政区，中国）；Department of Technology Management for Innovation, The University of Tokyo, Japan（创新技术管理系，东京大学，日本）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China（数据科学与人工智能系，香港理工大学，香港特别行政区，中国）
Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease
Taipei Medical University（台北医学大学）；University of Memphis（孟菲斯大学）；Chitkara University（奇特卡拉大学）；University College London（伦敦大学学院）；UPES（石油与能源研究大学）；University of Eastern Finland（东芬兰大学）；CNRS（法国国家科学研究中心）；University Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）
Vision-Language Models for Automated 3D PET/CT Report Generation
Institute of Medical Technology and National Biomedical Imaging Center, Peking University（北京大学医学技术研究院和国家生物医学成像中心）；Peking University People’s Hospital（北京大学人民医院）；Peking University Third Hospital（北京大学第三医院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；The First Affiliated Hospital of Henan Medical University（河南医科大学第一附属医院）；Jiujiang City Key Laboratory of Cell Therapy, Jiu Jiang NO.1 People’s Hospital（九江市细胞治疗重点实验室，九江市第一人民医院）
