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）
Abhejit Rajagopal, Yutaka Natsuaki, Kristen Wangerin, Mahdjoub Hamdi, Hongyu An, John J. Sunderland, Richard Laforest, Paul E. Kinahan, Peder E. Z. Larson, Thomas A. Hope
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 generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic $^{18}$F-FDG uptake, e.g. high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this synthetic PET data can be used interchangeably with real PET data for the PET quantification task of comparing CT and MR-based attenuation correction methods, achieving $\leq 7.6\%$ error in mean-SUV compared to using real data. These results together show that the proposed synthetic PET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.
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）
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…
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。
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…
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）；National Taiwan University（台湾大学）
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 readil…
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）
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…
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）
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…
Path and Bone-Contour Regularized Unpaired MRI-to-CT Translation
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China（计算机科学与技术学院，广东技术大学，广州，中国）；Neurological Institute, Cleveland Clinic, OH, USA（神经医学研究院，克利夫兰医学中心，俄亥俄州，美国）；Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China（神经外科，中山大学附属第一医院，广州，中国）；Brigham and Women’s Hospital, Harvard Medical School, MA, USA（布里奇沃特医院，哈佛医学院，马萨诸塞州，美国）；Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands（放射科，医学影像中心，格罗宁根大学医学中心，格罗宁根大学，荷兰）
Task-Adaptive 3D Cross-Field MRI Translation via Field-Conditioned Content-Style Pretraining
School of Integrated Circuits and Electronics, Beijing Institute of Technology（北京理工大学集成电路与电子学院）；School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）
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 …
Multi-modal MRI Translation via Evidential Regression and Distribution Calibration
Fudan University（复旦大学）；University of Cambridge（剑桥大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Fuzhou University（福州市大学）；Shanghai Jiao Tong University（上海交通大学）
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）
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）
Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static $q$-space sampling during training and reconstruction. Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary $q$-space…
GE Healthcare（通用电气医疗集团）；Umeå University（于默奥大学）；Erasmus MC Cancer Institute（伊拉斯谟医学中心癌症研究所）；Newcastle University（纽卡斯尔大学）；Newcastle upon Tyne Hospitals NHS Foundation Trust（泰恩河畔纽卡斯尔NHS基金会信托）；Technical University of Munich（慕尼黑工业大学）；Sree Chitra Tirunal Institute of Medical Sciences and Technology（斯里奇特拉蒂鲁纳尔医学科学与技术研究所）；University of Zurich（苏黎世大学）
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task network to produce correlated outputs as a framework to enable localisation of region of interest (RoI) via classification, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized…
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style sc…
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…
South China University of Technology（华南理工大学）；Rochester Institute of Technology（罗切斯特理工学院）
Due to the difficulties of obtaining multimodal paired images in clinical practice, recent studies propose to train brain tumor segmentation models with unpaired images and capture complementary information through modality translation. However, these models cannot fully exploit the complementary information from different modalities. In this work, we thus present a novel two-step (intra-modality and inter-modality) curriculum disentanglement learning framework to effectively utilize privileged semi-paired images, i.e. limited paired images that are only available in training, for brain tumor …
Ryerson University（瑞尔森大学）；University of Toronto（多伦多大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；St. Michael’s Hospital（圣迈克尔医院）；Keenan Research Centre for Biomedical Science（基南生物医学科学研究中心）；Institute for Biomedical Engineering, Science, and Technology (iBEST)（生物医学工程、科学与技术研究所（iBEST））
One of the key limitations in machine learning models is poor performance on data that is out of the domain of the training distribution. This is especially true for image analysis in magnetic resonance (MR) imaging, as variations in hardware and software create non-standard intensities, contrasts, and noise distributions across scanners. Recently, image translation models have been proposed to augment data across domains to create synthetic data points. In this paper, we investigate the application an unsupervised image translation model to augment MR images from a source dataset to a target …
Technical University of Munich（慕尼黑工业大学）；D’Annunzio University（邓南遮大学）
Convolutional networks are successful, but they have recently been outperformed by new neural networks that are equivariant under rotations and translations. These new networks work better because they do not struggle with learning each possible orientation of each image feature separately. So far, they have been proposed for 2D and 3D data. Here we generalize them to 6D diffusion MRI data, ensuring joint equivariance under 3D roto-translations in image space and the matching 3D rotations in $q$-space, as dictated by the image formation. Such equivariant deep learning is appropriate for diffus…
Bilkent University（比尔肯特大学）；University of Southern California（南加州大学）
Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images. To facilitate collaboration, federated learning (FL) adopts decentralized training and mitigates privacy concerns by avoiding sharing of imaging data. However, FL-trained synthesis models can be impaired by the inherent heterogeneity in the data distribution, with domain shifts evident when common or variable translation tasks are prescribed across sites. Here we introduce the first personalized FL method for MRI Synthesis (pFLSynth) to improve reliabil…
The integration of positron emission tomography (PET) and magnetic resonance imaging (MRI) has been an ongoing research topic for the last 20 years. This paper gives an overview of the different developments and the technical problems associated with combining PET and MRI in one system. After explaining the different detector concepts for integrating PET-MRI and minimising interference the limitations and advantages of different solutions for the detector and system are described for preclinical and clinical imaging systems. The different integrated PET-MRI systems are described in detail. Bes…
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. …
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Accurate and robust segmentation of lung cancers from CT, even those located close to mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure treatment response. Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby an informative teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background. Our contribution eliminates two requirements of distillation methods: (i) paired image sets by using an image to image (I2I) translation a…
Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（戈登医学影像中心，麻省总医院与哈佛医学院）；University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland School of Dentistry（马里兰大学牙医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Yale University（耶鲁大学）
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
SRUG: A Fusion-Driven Generator Network for Medical Image Translation
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子电气工程学院）；Department of Thoracic Surgery, Zhongshan Hospital of Fudan University（复旦大学中山医院胸外科）；Clinical Research Unit, Institute of Clinical Science, Zhongshan Hospital of Fudan University（复旦大学中山医院临床科研部）；Department of Medical Oncology, Cancer Center, and Fudan Zhangjiang Institute, Zhongshan Hospital of Fudan University（复旦大学中山医院医学肿瘤科、癌症中心及复旦张江研究院）
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…
Prostate cancer is the most common disease in men and the second leading cause of death from cancer. Generic large imaging instruments used in cancer diagnosis have sensitivity, spatial resolution, and contrast inadequate for the task of imaging details of a small organ such as the prostate. In addition, multimodality imaging can play a significant role merging anatomical and functional details coming from simultaneous PET and MRI. Indeed, multi-parametric PET/MRI was demonstrated to improve diagnosis, but it suffers from too many false positives. In order to address the above limits of the cu…
Purpose: To develop bone material analogues that can be used in construction of phantoms for simultaneous PET/MRI systems. Methods: Plaster was used as the basis for the bone material analogues tested in this study. It was mixed with varying concentrations of an iodinated CT contrast, a gadolinium-based MR contrast agent, and copper sulfate to modulate the attenuation properties and MRI properties (T1 and T2*). Attenuation was measured with CT and 68Ge transmission scans, and MRI properties were measured with quantitative ultrashort echo time pulse sequences. A proof-of-concept skull was creat…
MultiModal Imaging is an innovative technique that consists in the combination of diagnostic exams based on different physical processes, to obtain a unique image with more detailed clinical information. The possibility of a simultaneous use of PET and MRI could be achieved by using a particular radioisotope: 52gMn. The main route to produce this isotope is the reaction 52Cr(p,n)52gMn which is theoretically investigated in this study by means of three nuclear reaction codes. An analysis of the cross sections and a computation of the production rates and time evolution of the produced radioisot…
Patient motion is well-known for degrading image quality during medical imaging. Especially positron emission tomography (PET) is susceptible to motion due to its usually long scan times. In hybrid PET/MRI (magnetic resonance imaging), simultaneously acquired dynamic MRI data can be used to correct for motion. Usually, MRI model-based motion correction approaches are applied to the PET data. However, these approaches may fail for non-predictable, irregular motion. We propose a novel approach for the continuous and real-time tracking of motion using highly accelerated, dynamic MRI for an accura…
The integration of PET into MRI to form a hybrid system requires often compromises for both subsystems. For example, the integration might come at the cost of a reduced PET detector height or a reduced MRI examination volume diameter. Here, we propose a so-called shared-volume concept to use the volume required for both subsystems more efficiently, in which the MR transparency of the scintillator is exploited by integrating the RF shielding into the scintillator of the PET detector. Semi-monolithic scintillator prototypes (PVC slabs) of 7 mm and 12 mm height with integrated copper foil between…
A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of MRI artifacts (e.g. implants, motion) and uncertainties due to the limitations of MRI contrast (e.g. accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that, in addition to generating an initial pseudo-CT from MR data, also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with MLAA reconstruction that uses the PET em…
