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）
Yitong Li, Igor Yakushev, Dennis M. Hedderich, Christian Wachinger
Positron emission tomography (PET) is a widely recognized technique for diagnosing neurodegenerative diseases, offering critical functional insights. However, its high costs and radiation exposure hinder its widespread use. In contrast, magnetic resonance imaging (MRI) does not involve such limitations. While MRI also detects neurodegenerative changes, it is less sensitive for diagnosis compared to PET. To overcome such limitations, one approach is to generate synthetic PET from MRI. Recent advances in generative models have paved the way for cross-modality medical image translation; however, existing methods largely emphasize structural preservation while neglecting the critical need for pathology awareness. To address this gap, we propose PASTA, a novel image translation framework built on conditional diffusion models with enhanced pathology awareness. PASTA surpasses state-of-the-art methods by preserving both structural and pathological details through its highly interactive dual-arm architecture and multi-modal condition integration. Additionally, we introduce a novel cycle exchange consistency and volumetric generation strategy that significantly enhances PASTA's ability to produce high-quality 3D PET images. Our qualitative and quantitative results demonstrate the high quality and pathology awareness of the synthesized PET scans. For Alzheimer's diagnosis, the performance of these synthesized scans improves over MRI by 4%, almost reaching the performance of actual PET. Our code is available at https://github.com/ai-med/PASTA.
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）
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…
Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Model
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（斯蒂文斯神经影像与信息学研究所，凯克医学院，南加州大学）；Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California（明希部门电气与计算机工程系，维特比工程学院，南加州大学）；Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California（阿尔弗雷德·E·曼生物医学工程系，维特比工程学院，南加州大学）
University College London（伦敦大学学院）；Inria（法国国家信息与自动化研究所）；Université Côte d'Azur（蔚蓝海岸大学）
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets. DIVE clusters vertex-wise biomarker measurements on the cortical surface that have similar temporal dynamics across a patient population, and concurrently estimates an average trajectory of vertex measurements in each cluster. DIVE uniquely outputs a parcellation of the cortex into areas with common progression patterns, leading to a new signature…
Alzheimer's Disease (AD) is a progressive neurodegenerative disease. Amnestic mild cognitive impairment (MCI) is a common first symptom before the conversion to clinical impairment where the individual becomes unable to perform activities of daily living independently. Although there is currently no treatment available, the earlier a conclusive diagnosis is made, the earlier the potential for interventions to delay or perhaps even prevent progression to full-blown AD. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo view into the structure and func…
PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and TCGA Pathology
Friedrich-Alexander-Universität（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Klinikum Nürnberg, Paracelsus Medical University（帕拉塞尔苏斯医科大学纽伦堡医院）；Ludwig-Maximilians-Universität München（慕尼黑大学）；National Institute of Mental Health and Neurosciences (NIMHANS)（国家精神卫生与神经科学研究院）
CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach
Hanoi University of Science and Technology（河内科技大学）；Military Central Hospital（108军事中央医院）；VinUniversity；National Institute of Advanced Industrial Science and Technology（国立先进工业技术研究院）
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）
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data
University of Michigan-Shanghai Jiao Tong University Joint Institute（密歇根大学-上海交通大学联合学院）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术全球研究院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
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）
