Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke. Positron emission tomography (PET) is currently regarded as the gold standard for the measurement of CBF in the human brain. PET imaging, however, is not widely available because of its prohibitive costs, use of ionizing radiation, and logistical challenges, which require a co-localized cyclotron to deliver the 2 min half-life Oxygen-15 radioisotope. Magnetic resonance imaging (MRI), in contrast, is more readily available and does not involve ionizing radiation. In this study, we propose a multi-task learning framework for brain MRI-to-PET translation and disease diagnosis. The proposed framework comprises two prime networks: (1) an attention-based 3D encoder-decoder convolutional neural network (CNN) that synthesizes high-quality PET CBF maps from multi-contrast MRI images, and (2) a multi-scale 3D CNN that identifies the brain disease corresponding to the input MRI images. Our multi-task framework yields promising results on the task of MRI-to-PET translation, achieving an average structural similarity index (SSIM) of 0.94 and peak signal-to-noise ratio (PSNR) of 38dB on a cohort of 120 subjects. In addition, we show that integrating multiple MRI modalities can improve the clinical diagnosis of brain diseases.
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…
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）
Stanford University（斯坦福大学）；GE Healthcare（通用电气医疗集团）；University of California Riverside（加州大学河滨分校）
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (15O-water) is considered the gold-standard for the measurement of CBF in humans. PET imaging, however, is not widely available because of its prohibitive costs and use of short-lived radiopharmaceutical tracers that typically require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more readily accessible and does not involve ionizing radiation. This study presents a…
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…
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 …
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。
Recent studies suggest that combined analysis of Magnetic resonance imaging~(MRI) that measures brain atrophy and positron emission tomography~(PET) that quantifies hypo-metabolism provides improved accuracy in diagnosing Alzheimer's disease. However, such techniques are limited by the availability of corresponding scans of each modality. Current work focuses on a cross-modal approach to estimate FDG-PET scans for the given MR scans using a 3D U-Net architecture. The use of the complete MR image instead of a local patch based approach helps in capturing non-local and non-linear correlations be…
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）
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this paper we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly-available whole-body MRI. Specifically, we use a dataset of 56 $^{18}$F-FDG-PET/MRI exams to train a 3D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training we implemented a balanced loss function to gene…
Indian Institute of Science（印度科学院）；College of Engineering Trivandrum（特里凡得琅工程学院）
The revolutionary developments in the field of supervised machine learning have paved way to the development of CAD tools for assisting doctors in diagnosis. Recently, the former has been employed in the prediction of neurological disorders such as Alzheimer's disease. We propose a CAD (Computer Aided Diagnosis tool for differentiating neural lesions caused by CVA (Cerebrovascular Accident) from the lesions caused by other neural disorders by using Non-negative Matrix Factorisation (NMF) and Haralick features for feature extraction and SVM (Support Vector Machine) for pattern recognition. We a…
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）
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）
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）
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）
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）
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Massachusetts General Hospital, Harvard Medical School（马萨诸塞总医院，哈佛医学院）；University of Twente（特文特大学）；University of Copenhagen（哥本哈根大学）；University College London（伦敦大学学院）；Massachusetts Institute of Technology（麻省理工学院）；Danish Research Centre for Magnetic Resonance（丹麦磁共振研究中心）；Yale School of Medicine（耶鲁大学医学院）
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) automatic 3-D registration is implemented and validated for small animal image volumes so that the high-resolution anatomical MRI information can be fused with the low spatial resolution of functional PET information for the localization of lesion that is currently in high demand in the study of tumor of cancer (oncology) and its corresponding preparation of pharmaceutical drugs. Though many registration algorithms are developed and applied on human brain volumes, these methods may not be as efficient on small animal datas…
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
Martinos Center for Biomedical Imaging, MGH and Harvard Medical School（马蒂诺斯生物医学成像中心，麻省总医院与哈佛医学院）；Center for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital（哥本哈根大学医院丹麦磁共振研究中心）；Computer Science and Artificial Intelligence Laboratory, MIT（麻省理工学院计算机科学与人工智能实验室）
NeuroVascU-Net: A Unified Multi-Scale and Cross-Domain Adaptive Feature Fusion U-Net for Precise 3D Segmentation of Brain Vessels in Contrast-Enhanced T1 MRI
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（电信工程学院，工程学院，德黑兰大学）；Department of EECS, Lassonde School of Engineering, York University（电子工程系，拉索nde工程学院，约克大学）；Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences（神经外科系，医学院，伊朗医学科学大学）；Department of Electrical and Computer Engineering, University of Waterloo（电子工程系，滑铁库大学）
Cross-modality image estimation involves the generation of images of one medical imaging modality from that of another modality. Convolutional neural networks (CNNs) have been shown to be useful in identifying, characterising and extracting image patterns. Generative adversarial networks (GANs) use CNNs as generators and estimated images are discriminated as true or false based on an additional network. CNNs and GANs within the image estimation framework may be considered more generally as deep learning approaches, since imaging data tends to be large, leading to a larger number of network wei…
Sathyabama University（萨蒂亚巴马大学）；Narasu’s Sarathy Institute of Technology（纳拉苏萨拉蒂技术学院）
Medical image segmentation is referred to the segmentation of known anatomic structures from different medical images. Normally, the medical data researches are more complicated and an exclusive structures. This computer aided diagnosis is used for assisting doctors in evaluating medical imagery or in recognizing abnormal findings in a medical image. To integrate the specialized knowledge for medical data processing is helpful to form a real useful healthcare decision making system. This paper studies the different symmetry based distances applied in clustering algorithms and analyzes symmetry…
EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI
Yozgat Bozok University（亚兹加特博兹克大学）；Izmir Bakircay University（伊兹米尔巴克伊大学）；Queen Mary’s Digital Environment Research Institute (DERI)（皇后玛丽数字环境研究 institute (DERI)）；Firat University（费拉特大学）；Qatar University（卡塔尔大学）
Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. …
NVIDIA Corporation（英伟达公司）；Seoul National University（首尔大学）
Synthesizing medical images, such as PET, is a challenging task due to the fact that the intensity range is much wider and denser than those in photographs and digital renderings and are often heavily biased toward zero. Above all, intensity values in PET have absolute significance, and are used to compute parameters that are reproducible across the population. Yet, usually much manual adjustment has to be made in pre-/post- processing when synthesizing PET images, because its intensity ranges can vary a lot, e.g., between -100 to 1000 in floating point values. To overcome these challenges, we…
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）
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…
University of California, San Francisco（加州大学旧金山分校）；University of California, Berkeley（加州大学伯克利分校）；University of Colorado（科罗拉多大学）
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease. These reconstruction approaches utilize priors, either structural or statistical, together with a physics-based description of the PET system response. However, due to the nested representation of the forward problem, direct PET/MRI reconstruction is a nonlinear problem. We elucidate how a multi-faceted approach accom…
Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration
Department of Computer Science, Yonsei University（延世大学计算机科学系）；Department of Artificial Intelligence, Yonsei University（延世大学人工智能系）；Yonsei University College of Medicine（延世大学医学院）
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Université de Rouen Normandie（鲁昂诺曼底大学）；Centre Henri Becquerel（亨利·贝克勒尔中心）；Institut universitaire de France（法国大学研究院）
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Research center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Nanchang University（南昌大学）；University at Buffalo, the State University of New York（纽约州立大学布法罗分校）
Medical imaging is playing a more and more important role in clinics. However, there are several issues in different imaging modalities such as slow imaging speed in MRI, radiation injury in CT and PET. Therefore, accelerating MRI, reducing radiation dose in CT and PET have been ongoing research topics since their invention. Usually, acquiring less data is a direct but important strategy to address these issues. However, less acquisition usually results in aliasing artifacts in reconstructions. Recently, deep learning (DL) has been introduced in medical image reconstruction and shown potential…
Indian Institute of Technology (IIT) Bombay（印度理工学院孟买分校）；IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什研究学院）；Monash University（莫纳什大学）
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, s…
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）
Images from Positron Emission Tomography (PET) deliver functional data such as perfusion and metabolism. On the other hand, images from Magnetic Resonance Imaging (MRI) provide information describing anatomical structures. Fusing the complementary information from the two modalities is helpful in oncology. In this project, we implemented a complete tool allowing semi-automatic MRI-PET registration for small animal imaging in the preclinical studies. A two-stage hierarchical registration approach is proposed. First, a global affine registration is applied. For robust and fast registration, prin…
Cross Modality Image Translation In Medical Imaging Using Generative Frameworks
Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入部门、放射物理、生物医学工程，乌梅大学）；Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma（人工智能与计算机系统单位，工程部门，罗马生物医学学院）；Vita-Salute San Raffaele University（维塔-萨拉特·桑拉法埃莱大学）；Department of Medicine, Surgery and Dentistry, University of Salerno（医学、外科和牙科部门，萨勒诺大学）；Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel（诊断和介入神经放射学部门，放射学部门，巴塞尔大学医院）；Department of Pediatric Radiology, University Children’s Hospital Basel（儿科放射学部门，巴塞尔儿童医院）；Department of Life Science and Public Health, Università Cattolica del Sacro Cuore（生命科学与公共健康部门，圣心大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿提诺拉A·马里诺斯生物医学成像中心）；Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete（人工智能与转化成像（ATI）实验室，放射学部门，医学院，克里特大学）；Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute（放射学部门，临床科学、介入和科技（CLINTEC）部门，卡罗林斯卡研究所）；Columbia University Medical Center（哥伦比亚大学医学中心）；Department of Diagnostics and intervention, Diagnostic radiology, Umeå University（诊断与介入部门，诊断放射学，乌梅大学）
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 …
