Purpose: In this study we investigate whether a Convolutional Neural Network (CNN) can generate clinically relevant parametric maps from CT perfusion data in a clinical setting of patients with acute ischemic stroke. Methods: Training of the CNN was done on a subset of 100 perfusion data, while 15 samples were used as validation. All the data used for the training/validation of the network and to generate ground truth (GT) maps, using a state-of-the-art deconvolution-algorithm, were previously pre-processed using a standard pipeline. Validation was carried out through manual segmentation of infarct core and penumbra on both CNN-derived maps and GT maps. Concordance among segmented lesions was assessed using the Dice and the Pearson correlation coefficients across lesion volumes. Results: Mean Dice scores from two different raters and the GT maps were > 0.70 (good-matching). Inter-rater concordance was also high and strong correlation was found between lesion volumes of CNN maps and GT maps (0.99, 0.98). Conclusion: Our CNN-based approach generated clinically relevant perfusion maps that are comparable to state-of-the-art perfusion analysis methods based on deconvolution of the data. Moreover, the proposed technique requires less information to estimate the ischemic core and thus might allow the development of novel perfusion protocols with lower radiation dose.
Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks
Novosibirsk State University（新西伯利亚国立大学）；International Tomography Center SB RAS（西伯利亚分院国际断层扫描中心）；Sobolev Institute of Mathematics SB RAS（西伯利亚分院索博列夫数学研究所）
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
University of Bern（伯尔尼大学）；Department of Neurology, Inselspital, University of Bern（伯尔尼大学 Inselspital 神经科）；Department of Neurology, University of California Los Angeles (UCLA)（加州大学洛杉矶分校（UCLA）神经科）；Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern（伯尔尼大学 Inselspital 诊断与介入神经放射学研究所高级神经影像支持中心）
Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model
Kobe University Hospital（神户大学医院）；Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先进科学技术研究中心）；Ichinomiyanishi Nishi Hospital（一宮西医院）；Osaka Red Cross Hospital（大阪红十字医院）
背景和目的：我们的目标是开发和评估一种涉及两阶段深度学习模型的自动急性缺血性卒中相关（AIS）检测系统。方法：我们纳入了来自两个不同机构的238例病例。通过参考头部磁共振成像（MRI）图像，在CT扫描后24小时内进行MRI检查，在238组头部CT图像中的每一组上注释AIS相关结果。这238个标注的案例被分成包括189个案例的训练集和包括49个案例的测试集。随后，使用You Only Look Once v3模型和Visual Geometry Group 16分类模型从训练集构建了两阶段深度学习检测模型。然后，两阶段模型在测试集中执行AIS检测过程。为了评估检测模型的结果，委员会认证的放射科医生还在有和没有检测模型的帮助下评估了测试集的头部CT图像。计算AIS检测的灵敏度和假阳性数，以评价测试集检测结果。使用McNemar检验比较了放射科医生在有和没有软件检测结果的情况下的灵敏度。p值小于0.05被认为具有统计学显著性。结果如下：对于两阶段模型和放射科医生，没有和使用软件结果，灵敏度分别为37.3%，33.3%和41.3%，每例病例的假阳性数分别为1.265，0.327和0.388。在使用两阶段检测模型的结果时，委员会认证的放射科医生的检测灵敏度显著提高（p值= 0.0313）。结论：我们的检测系统涉及两阶段深度学习模型，显着提高了放射科医生在AIS检测中的灵敏度。
Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University（麻醉学、围术期与疼痛医学系，斯坦福大学）；Department of Biomedical Sciences, Seoul National University（生物医学科学系，首尔国立大学）；Department of Computer Engineering, National University of Sciences and Technology (NUST)（计算机工程系，国立科学与技术大学（NUST））；Department of Computer Science, The University of Alabama（计算机科学系，阿拉巴马大学）；Department of Computer Science, Hanyang University（计算机科学系，翰阳大学）
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning
Queens College of the City University of NY（纽约市立大学皇后学院）；National Chung Cheng University（台湾中正大学）；Ditmanson Medical Foundation Chia-Yi Christian Hospital（戴德森医疗财团法人嘉义基督教医院）
Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
University of Electronic Science and Technology of China（电子科技大学）；SenseTime Research（商汤科技研究院）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Fudan University（复旦大学）；Jing’an District Centre Hospital of Shanghai（上海市静安区中心医院）
Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images
Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院模式识别与智能系统实验室）；Deepwise AI Lab（深度科技人工智能实验室）；Jinling Hospital, Nanjing University School of Medicine（南京大学医学院附属金陵医院）；Department of Computer Science and Technology, Peking University（北京大学计算机科学技术系）；The University of Hong Kong（香港大学）
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右岸医院）；School of Medicine, Technical University of Munich（慕尼黑工业大学医学院）；icometrix（icometrix公司）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；University of Bern（伯尔尼大学）；ARTORG Center for Biomedical Engineering Research, Univ. of Bern（伯尔尼大学生物医学工程研究ARTORG中心）；University of Southern California（南加州大学）；Chan Division of Occupational Science and Occupational Therapy, University of Southern California（南加州大学陈职业科学与职业治疗系）；TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学TranslaTUM转化癌症研究中心）
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）
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results
The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center（西德尼·金梅尔综合癌症中心）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Quantitative perfusion maps using a novelty spatiotemporal convolutional neural network
School of Applied Technology, Shenzhen University（深圳大学应用技术学院）；College of Health Science and Environmental Engineering, Shenzhen Technology University（深圳技术大学健康科学与环境工程学院）；Department of Biomedical Engineering, Columbia University（哥伦比亚大学生物医学工程系）；Zuckerman Institute, Columbia University（哥伦比亚大学扎克曼研究所）；College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine（同济大学医学院附属上海市第四人民医院）
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）
DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT
Department of Electrical and Computer Engineering, UCLA（电气与计算机工程系，加州大学洛杉矶分校）；Medical Informatics, UCLA（医学信息学，加州大学洛杉矶分校）；Department of Radiological Sciences, UCLA（放射科学系，加州大学洛杉矶分校）；Department of Radiology, UC San Francisco（放射科，旧金山大学医学院）；Department of Bioengineering, UCLA（生物工程系，加州大学洛杉矶分校）；Department of Pathology and Laboratory Medicine, UCLA（病理学与实验室医学系，加州大学洛杉矶分校）
Comparing Baseline and Day-1 Diffusion MRI Using Multimodal Deep Embeddings for Stroke Outcome Prediction
1 Department of Computer Science, University of Geneva, Switzerland 2 Department of Electrical \& Computer Engineering, University of Florida, FL, USA 3 Service of Medical Informatics, University Hospital of Geneva, Switzerland 4 Department of Imaging；Medical Informatics, University of Geneva, Switzerland
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks
Center for Advanced Medical Computing and Analysis（高级医学计算与分析中心）；Gordon Center for Medical Imaging（戈登医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
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）
ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.；Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.；ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland.；Department of Radiation Oncology, University Hospital Bern, University of Bern.；Support Center of Advanced Neuroimaging (SCAN), University Institute of Diagnostic；University Institute of Diagnostic；Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland.；Department of Neurology, University Hospital of Zurich, Zurich, Switzerland.；University of Zurich, Zurich, Switzerland.；Department of Diagnostic；Interventional Neuroradiology, School of Medicine；Health, TUM Klinikum, Technical University of Munich, Germany.；TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Germany.；Therapy, School of Medicine；Health, Technical University of Munich, Munich, Germany.；Department of Biomedical Engineering；Physics, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands；Department of Radiology, University of Calgary, Calgary, Canada；Hotchkiss Brain Institute, University of Calgary, Calgary, Canada；Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada；Heuron Co., Ltd., Seoul, South Korea；School of Electrical Engineering, Korea University, Seoul, Korea；University of Alberta, Alberta, Canada；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom；Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom；University of Calgary, Canada；University of Washington, Washington, United States；CREATIS, Universite Claude Bernard Lyon 1, INSA Lyon, UMR CNRS 5220, Inserm U1294, Villeurbanne, France；Department of Neurology, University Hospital Zurich, Zurich, Switzerland
