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 limited. Thus, it is desirable to develop an efficient multi-tracer PET synthesis model that can generate multi-tracer PET from single-tracer PET. Previous works on medical image synthesis focus on one-to-one fixed domain translations, and cannot simultaneously learn the feature from multi-tracer domains. Given 3 or more tracers, relying on previous methods will also create a heavy burden on the number of models to be trained. To tackle these issues, we propose a 3D unified anatomy-aware cyclic adversarial network (UCAN) for translating multi-tracer PET volumes with one unified generative model, where MR with anatomical information is incorporated. Evaluations on a multi-tracer PET dataset demonstrate the feasibility that our UCAN can generate high-quality multi-tracer PET volumes, with NMSE less than 15% for all PET tracers.
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）
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）
NVIDIA Corporation（英伟达公司）；Seoul National University（首尔大学）
Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest dosage of radiation. Magnetic Resonance Imaging (MRI), in contrast, is more widely available and provides more flexibility when setting the desired image resolution. Unfortunately, the diagnosis of AD using MRI is difficult due to the very subtle physiological differences between healthy and AD subjects visible on MRI. As a result, many attempts have…
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）
While Positron emission tomography (PET) imaging has been widely used in diagnosis of number of diseases, it has costly acquisition process which involves radiation exposure to patients. However, magnetic resonance imaging (MRI) is a safer imaging modality that does not involve patient's exposure to radiation. Therefore, a need exists for an efficient and automated PET image generation from MRI data. In this paper, we propose a new frequency-aware attention U-net for generating synthetic PET images. Specifically, we incorporate attention mechanism into different U-net layers responsible for es…
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Shandong University（山东大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible …
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）
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis. However, there is a lack of clear understanding of why and how the Convolutional Neural Network performs so well for image analysis task. How CNN analyzes an image and discriminates among samples of different classes are usually considered as non-transparent. As a result, it becomes difficult to apply CNN based approaches in clinical procedures and automated disease diagnosis systems. In this paper, we consider this issue and work on visualizing and understanding the decision…
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）
Lab for Artificial Intelligence in Medical Imaging (AI-Med), KJP, LMU Klinikum（慕尼黑大学附属医院儿童与青少年精神病学系医学影像人工智能实验室）；Technical University of Munich, School of Medicine（慕尼黑工业大学医学院）
Alzheimer's Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that fusing structural magnetic resonance images (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) leads to improved accuracy in a study population of healthy controls and subjects with AD. However, this result conflicts with the established clinical knowledge that FDG-PET better captures AD-specific pathologies than sMRI. Therefore, we p…
Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, USA（华盛顿大学医学院马林克罗德特放射医学研究所，圣路易斯，美国）；Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St. Louis, USA（华盛顿大学圣路易斯分校麦克雷高中工程学院成像科学计划，圣路易斯，美国）；Department of Surgery, Washington University School of Medicine, St. Louis, USA（华盛顿大学医学院外科部，圣路易斯，美国）；Department of Biomedical Engineering, Washington University in St Louis, St. Louis, USA（华盛顿大学圣路易斯分校生物医学工程部，圣路易斯，美国）
Shanghai University（上海大学）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；University of Science and Technology of China（中国科学技术大学）；Nanjing Medical University（南京医科大学）；The University of Electronic Science and Technology of China（电子科技大学）
FDG-PET reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, the studies of identification of early MCI (EMCI) and late MCI (LMCI) with FDG-PET images are still insufficient. Compared with studies based on fMRI and DTI images, the researches of the inter-region representation features in FDG-PET images are insufficient. Moreover, considering the variability…
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）
Yuanpei College Peking University（北京大学元培学院）；Massachusetts General Hospital（麻省总医院）；College of Computer Science Zhejiang University（浙江大学计算机学院）
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep learning framework for PET image analysis to overcome its limitation on Euclidean grid, we develop a so…
A large amount of manual segmentation is typically required to train a robust segmentation network so that it can segment objects of interest in a new imaging modality. The manual efforts can be alleviated if the manual segmentation in one imaging modality (e.g., CT) can be utilized to train a segmentation network in another imaging modality (e.g., CBCT/MRI/PET). In this work, we developed an anatomy-constrained contrastive synthetic segmentation network (AccSeg-Net) to train a segmentation network for a target imaging modality without using its ground truth. Specifically, we proposed to use a…
Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation
Department of diagnostic and interventional Radiology and Neuroradiology, University Hospital Augsburg（诊断与介入放射学及神经放射学系，奥格斯堡大学医院）；Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich（计算机辅助医疗程序与增强现实，慕尼黑技术大学）；Digital Medicine, University Hospital Augsburg（数字医学，奥格斯堡大学医院）；Center of Advanced Analytics and Predictive Sciences, University of Augsburg（高级分析与预测科学中心，奥格斯堡大学）
Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
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）
Technical University Munich（慕尼黑工业大学）；Ludwig Maximilians University Munich（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagnosis, FDG-PET is not always available. The reliable detection of Alzheimer's disease with only MRI could be beneficial, especially in regions where FDG-PET might not be affordable for all patients. To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classif…
Modality-Agnostic Style Transfer for Holistic Feature Imputation
Pohang University of Science and Technology（浦项科技大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of North Carolina at Greensboro（北卡罗来纳大学格林斯伯勒分校）
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Biomedical Informatics（生物医学信息学系）；Department of Neurology（神经病学系）；Translational Data Analytics Institute（转化数据分析研究所）
Lehigh University（里海大学）；University of Pennsylvania（宾夕法尼亚大学）
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting…
Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA（计算与增强智能学院，亚利桑那州立大学）；Amazon AGI, Redmond, WA, USA（亚马逊人工智慧部门）；Banner Alzheimer’s Institute, Phoenix, AZ, USA（贝纳尔德阿尔茨海默病研究所）
Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging
AI Graduate School, Gwangju Institute of Science and Technology（光州科学技术院人工智能研究生院）；Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea（韩国天主教大学医学院首尔圣玛丽医院）；Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea（韩国天主教大学医学院汝矣岛圣玛丽医院）
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）
University of Pennsylvania（宾夕法尼亚大学）；University of Texas San Antonio Health Science Center（德克萨斯大学圣安东尼奥健康科学中心）；National Institute on Aging（美国国家老龄化研究所）
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a novel semi-supervised deep-clustering method, which dissects neuroanatomical heterogeneity, enabling identification of disease subtypes via their imaging signatures relative to controls. When applied to MRIs (2 studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified 4 neurodegenerative patterns/axes: P1, normal anatomy a…
Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación（信息与电信工程高等技术学院）
Currently, the diagnosis of Alzheimer's disease is a complex and error-prone process. Improving this diagnosis could allow earlier detection of the disease and improve the quality of life of patients and their families. For this work, we will use 249 brain images from two modalities: PET and MRI, taken from the ADNI database, and labelled into three classes according to the degree of development of Alzheimer's disease. We propose the development of a convolutional neural network to perform the classification of these images, during which, we will study the appropriate depth of the networks for…
Biomediq A/S（Biomediq公司）；Cerebriu A/S（Cerebriu公司）；University of Copenhagen（哥本哈根大学）；University College London（伦敦大学学院）；King’s College London（伦敦国王学院）
Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated…
ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
1 Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China 2 Amazon, USA 3 Shanghai Artificial Intelligence Laboratory, China 4 Department of Geriatrics, Third Affiliated Hospital of Sun Yat-sen University, China 5 Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
针对 AD 分析中多模态、任意模态组合及缺失输入的任务需求，论文提出基于 LLM 的专用智能体 ADAgent，通过推理引擎、医学工具与协作结果协调器提升诊断和预后性能。
A Surface-based Multimodal Framework for Multitask Analysis in Alzheimer's Disease
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC)（南加州大学凯克医学院史蒂恩斯神经影像与信息学研究所）；Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California (USC)（南加州大学维特比工程学院阿尔弗雷德·E·曼生物医学工程系）；Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC)（南加州大学维特比工程学院明海电气与计算机工程系）
提出一种增强的球形数据驱动多模态框架，通过球形扩散模型生成配对皮层厚度和Tau PET SUVR数据，结合对比学习和上下文学习，实现多任务分类与回归，在ADNI数据集上优于六种基线模型。
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（大脑研究所 - 巴黎大脑研究所 - ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié Salpêtrière（皮蒂-萨尔皮特里耶医院）
针对脑部 FDG PET 无监督异常检测中残差图像方法的局限性，提出受 Z-scores 启发、利用健康人群变异性的新方法，在 ADNI 数据库上证明其能准确识别阿尔茨海默病相关异常。
