School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；The Alan Turing Institute（阿兰·图灵研究所）；Philips Healthcare（飞利浦医疗保健）；Eindhoven University of Technology（埃因霍温理工大学）
Cian M. Scannell, Amedeo Chiribiri, Adriana D. M. Villa, Marcel Breeuwer, Jack Lee
Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data. However, the model-fitting can produce unreliable results due to the limited data acquired and the high noise levels. Such problems are especially prevalent in myocardial perfusion MRI leading to the compromise of constrained numerical deconvolutions and segmental signal averaging being commonly used as alternatives to the more complex tracer-kinetic models. Methods: In this work, the use of hierarchical Bayesian inference for the parameter estimation is explored. It is shown that with Bayesian inference it is possible to reliably fit the two-compartment exchange model to perfusion data. The use of prior knowledge on the ranges of kinetic parameters and the fact that neighbouring voxels are likely to have similar kinetic properties combined with a Markov chain Monte Carlo based fitting procedure significantly improves the reliability of the perfusion estimates with compared to the traditional least-squares approach. The method is assessed using both simulated and patient data. Results: The average (standard deviation) normalised mean square error for the distinct noise realisations of a simulation phantom falls from 0.32 (0.55) with the least-squares fitting to 0.13 (0.2) using Bayesian inference. The assessment of the presence of coronary artery disease based purely on the quantitative MBF maps obtained using Bayesian inference matches the visual assessment in all 24 slices. When using the maps obtained by the least-squares fitting, a corresponding assessment is only achieved in 16/24 slices. Conclusion: Bayesian inference allows a reliable, fully automated and user-independent assessment of myocardial perfusion on a voxel-wise level using the two-compartment exchange model.
Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning
National Heart, Lung and Blood Institute, National Institutes of Health（美国国立卫生研究院国家心肺血液研究所）；Barts Health NHS Trust（巴特保健国民保健信托基金）；University of Leeds（利兹大学）；Royal Free Hospital（皇家自由医院）
Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws
Department of Bioengineering, Imperial College London（生物工程系，帝国理工学院伦敦分校）；National Heart and Lung Institute, Imperial College London（国家心脏和肺 institute，帝国理工学院伦敦分校）；Guy’s and St. Thomas’ NHS Foundation Trust（圣泰莫斯国家健康服务信托基金）
Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Zhengzhou University of Light Industry（郑州轻工业大学）；Michigan Technological University（密歇根理工大学）；Xi'an University of Posts and Telecommunications（西安邮电大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image
Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）；Xiangya Hospital, Central South University（中南大学湘雅医院）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）
Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis
Indiana University School of Medicine（印第安纳大学医学院）；Purdue University（普渡大学）；University of Calgary（卡尔加里大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Duke University（杜克大学）；The Ohio State University（俄亥俄州立大学）；Weill Cornell Medical Center（威尔康奈尔医学中心）；OhioHealth（俄亥俄健康）
Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images
Michigan Technological University（密歇根理工大学）；Hai’an People’s Hospital（海安市人民医院）；Rush Medical College（拉什医学院）；Cook County Health（库克县医疗系统）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Xi’an University of Posts and Telecommunications（西安邮电大学）
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
University of Notre Dame（圣母大学）；Boston Children’s Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital（广东省人民医院广东省心血管病研究所）；University of Pittsburgh（匹兹堡大学）
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
INSA-Lyon, Universite Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294（里昂国立应用科学学院、里昂第一大学克洛德·贝尔纳大学、圣艾蒂安大学、法国国家科学研究中心、法国国家健康与医学研究院、CREATIS联合研究机构）；Institut Universitaire de France (IUF)（法兰西大学研究院）；University of Sherbrooke（谢布鲁克大学）；Huazhong University of Science and Technology（华中科技大学）；Sunnybrook Research Institute, University of Toronto（多伦多大学桑尼布鲁克研究所）；Imperial College London（伦敦帝国理工学院）；University College London（伦敦大学学院）；Fudan University（复旦大学）；Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨技术大学）；Universidad de Zaragoza（萨拉戈萨大学）；Instituto de Investigación Sanitaria de Aragón (IIS Aragón)（阿拉贡卫生研究所）；Centre Hospitalier Annecy Genevois（阿讷西日内瓦中心医院）；Hôpital Universitaire Cardiologique Louis Pradel（路易·普拉德尔大学心脏病医院）
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control
King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；ETH Zürich（苏黎世联邦理工学院）；Imperial College London（伦敦帝国理工学院）
Reconstructing Quantitative Cerebral Perfusion Images Directly From Measured Sinogram Data Acquired Using C-arm Cone-Beam CT
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Southeast University（东南大学）；United Imaging Healthcare Limited Company（联影医疗科技股份有限公司）
MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images
University of Leicester（莱斯特大学）；NIHR Leicester Biomedical Research Centre（NIHR莱斯特生物医学研究中心）；Glenfield Hospital（格伦菲尔德医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；Saint-Petersburg State Electrotechnical University（圣彼得堡国立电工大学）
Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI
Universidad de Valladolid（巴利亚多利德大学）；King’s College London（伦敦国王学院）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Centre for Marine Sciences - CCMAR（海洋科学中心（CCMAR））
ScarNet: A Novel Foundation Model for Automated Myocardial Scar Quantification from LGE in Cardiac MRI
Northwestern University（西北大学）；Feinberg School of Medicine（费林伯格医学院）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI
Department of Acute Brain（急性脑和心血管损伤部门）；Department of Neuroscience（神经科学部门）；Istituto di Ricerche Farmacologiche Mario Negri IRCCS（马里奥·内格里药理研究所）；Milan（米兰）；Italy（意大利）
