检索结果-内蒙古大学图书馆

Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

学校读者我要写书评

暂无评论

Brain‐X

Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

E-service quality from attributes to outcomes: The similarity and difference between digital and hybrid services

学校读者我要写书评

暂无评论

Journal of Open Innovation: Technology, Market, and Complexity 2020年第4期6卷 1-21页

作者： Vatolkina, Natalia Gorbashko, Elena Kamynina, Nadezhda Fedotkina, Olga Department of Management Bauman Moscow State Technical University (National Research University) Moscow 105005 Russian Federation Department of Project and Quality Management Saint Petersburg State University of Economics St. Petersburg 191023 Russian Federation Department of Land Law and State Registration of Real Estate Moscow State University of Geodesy and Cartography Moscow 105064 Russian Federation Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow 125167 Russian Federation

Our research goal is to offer an e-service quality model based on experience and multidimensional quality and compare its applicability for e-services to find differences and similarities in consumer perceptions and behavioral intentions. Additionally, we seek to compare attributes that compose quality dimensions for hybrid and digital e-services. The study was based on an online survey conducted in July–September 2019 among citizens and foreign residents in the Russian Federation. Respondents had to answer questions concerning a specific e-service brand to capture real consumer behavior. The data of 365 questionnaires were analyzed using the Spearman correlation to determine the relationship between the model components. Customer experience is a valid outcome variable in the e-service model that strongly influences customer satisfaction and repurchase intentions. The model proved to be equally valid for hybrid and digital e-services. The key differences between digital and hybrid e-services lie within the distribution of e-service attributes between quality dimensions. Ease of use and perceived usefulness are the most essential attributes that have a direct influence on customer satisfaction. The findings show the necessity of best practices diffused between different types of e-services and present an opportunity to widely spread research findings between different e-service sectors. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Customer experience E-service E-service quality

Associations of road traffic noise with adipose tissue depots and hepatic fat content - Results from the German National Cohort (NAKO)

学校读者我要写书评

暂无评论

Environment international 2025年 201卷 109566页

作者： Fiona Niedermayer Susanne Rospleszcz Clara Matthiessen Barbara Hoffmann Sophia Stoecklein Tobias Haueise Tobias Norajitra Christopher L Schlett Johanna Nattenmüller Fabian Bamberg Jürgen Machann Matthias Günther Jochen Hirsch Rajini Nagrani Henry Völzke Claudia Meinke-Franze Norbert Hosten Tobias Nonnenmacher Bettina Katalin Budai Viktoria Palm Verena Katzke Karin Halina Greiser Jeanette Schulz-Menger Thoralf Niendorf Beate Endemann Tobias Pischon Jeroen Staab Marco Dallavalle Alexandra Schneider Kathrin Wolf Annette Peters Chair of Epidemiology IBE Faculty of Medicine Ludwig-Maximilians-Universität Munich Munich Germany Institute of Epidemiology Helmholtz Zentrum München (GmbH) German Research Center for Environmental Health Neuherberg Germany. Electronic address: fiona.niedermayer@helmholtz-munich.de. Institute of Epidemiology Helmholtz Zentrum München (GmbH) German Research Center for Environmental Health Neuherberg Germany Department of Diagnostic and Interventional Radiology Medical Center University of Freiburg Freiburg Germany. Institute of Occupational Social and Environmental Medicine Medical Faculty and University Hospital Heinrich Heine University Düsseldorf Düsseldorf Germany. Department of Radiology University Hospital of Munich LMU Munich Munich Germany. Institute for Diabetes Research and Metabolic Diseases Helmholtz Munich at the University of Tübingen Tübingen Germany German Center for Diabetes Research (DZD) Neuherberg Germany. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology University Hospital Heidelberg Germany. Department of Diagnostic and Interventional Radiology Medical Center University of Freiburg Freiburg Germany Institute for Radiology and Nuclear Medicine Hirslanden Clinic St. Anna Lucerne Switzerland. German Center for Diabetes Research (DZD) Neuherberg Germany Section on Experimental Radiology Department for Diagnostic and Interventional Radiology University Hospital Tübingen Tübingen Germany. Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany. Department Epidemiological Methods and Etiological Research Leibniz Institute for Prevention Research and Epidemiology - BIPS Bremen Germany. Institute for Community Medicine University Medicine Greifswald Greifswald Germany. Department of Radiology Universitätsmedizin Greifswald Greifswald Germany. Department of Diagnostic and Interventional Radiology Heidelberg University Hospit

Little is known about the relation between traffic noise exposure, an established environmental risk factor for cardiovascular disease, and early obesity-related risk markers such as adipose tissue (AT) and hepatic fat. Therefore, we aimed to assess associations of long-term road traffic noise exposure with AT depots measures from whole-body magnetic resonance imaging (MRI). We analyzed cross-sectional data from 11,343 participants from the population-based German National Cohort (NAKO) who underwent MRI examination between 2014 and 2016, considering visceral (VAT), subcutaneous abdominal (SCAAT), subcutaneous thoracic AT (SCTAT) and hepatic fat content as outcomes. Annual road traffic noise (L) data from the year 2017 (source: central EIONET data repository) was used to calculate weighted mean noise levels on a continuous scale within 10 and 100-meter buffers of participants' residencies. Among 11,101 participants with complete outcome data, 48.7 % were women, and the mean age was 51.9 years. Higher annual L was associated with increased AT depots and hepatic fat content in men (e.g., VAT: 1.72 %-change [95 % confidence interval: [0.14 %; 3.30 %]; SCAAT: 2.18 %-change [0.43 %; 3.93 %], hepatic fat content: 3.57 %-change [1.41 %; 5.78 %] per 10 dB(A) increase in L (10 m)) and women (e.g., VAT: 3.13 %-change [1.09 %; 5.18 %]; SCAAT: 2.38 %-change [0.55 %; 4.20 %], hepatic fat content: 3.08 %-change [1.00 %; 5.21 %] per 10 dB(A) increase in L (10 m)). Associations were robust with all outcomes after adjusting for air pollutants and surrounding greenness, and effect modification by obesity and hypertension was observed for SCAAT, SCTAT and hepatic fat content. Our findings indicate that annual exposure to road traffic noise is associated with increased adipose tissue depots and hepatic fat content, and thus present novel evidence for the cross-sectional association between noise and early MRI-derived metabolic health markers.

关键词： Environment Hepatic steatosis Metabolic disease Obesity

Correction: Understanding overfitting in random forest for probability estimation: a visualization and simulation study

学校读者我要写书评

暂无评论

Diagnostic and prognostic research 2025年第1期9卷 9页

作者： Lasai Barreñada Paula Dhiman Dirk Timmerman Anne-Laure Boulesteix Ben Van Calster Department of Development and Regeneration Louvain KU Belgium. Leuven Unit for Health Technology Assessment Research (LUHTAR) Louvain KU Belgium. Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. Department of Obstetrics and Gynecology University Hospitals Leuven Louvain Belgium. Institute for Medical Information Processing Biometry and Epidemiology Faculty of Medicine LMU Munich Munich Germany. Munich Center for Machine Learning Munich Germany. Department of Development and Regeneration Louvain KU Belgium. ben.vancalster@kuleuven.be. Leuven Unit for Health Technology Assessment Research (LUHTAR) Louvain KU Belgium. ben.vancalster@kuleuven.be. Department of Biomedical Data Sciences Leiden University Medical Centre Leiden the Netherlands. ben.vancalster@kuleuven.be.

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

Lessons Learned from Assessing Trustworthy AI in Practice

学校读者我要写书评

暂无评论

Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

关键词：

The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Kofler, Florian Steinbauer, Felix Meissen, Felix Graf, Robert Ehrlich, Stefan K. Reinke, Annika Oswald, Eva Waldmannstetter, Diana Hoelzl, Florian Horvath, Izabela Turgut, Oezguen Shit, Suprosanna Bukas, Christina Yang, Kaiyuan Paetzold, Johannes C. de da Rosa, Ezequiel Mekki, Isra Vinayahalingam, Shankeeth Kassem, Hasan Zhang, Juexin Chen, Ke Weng, Ying Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Sadique, M.S. Rahman, M.M. Farzana, W. Temtam, A. Iftekharuddin, K.M. Adewole, Maruf Anwar, Syed Muhammad Baid, Ujjwal Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Bran Li, Hongwei Moawad, Ahmed W. Conte, Gian-Marco Farahani, Keyvan Eddy, James Sheller, Micah Pati, Sarthak Karagyris, Alexandros Aristizabal, Alejandro Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Johanson, Elaine Meier, Zeke Familiar, Ariana Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva-Meyer, Javier Colak, Errol Crivellaro, Priscila Jakab, Andras Fatade, Abiodun Omidiji, Olubukola Akinola Lagos, Rachel Olatunji, O.O. Khanna, Goldey Kirkpatrick, John Alonso-Basanta, Michelle Rashid, Arif Bornhorst, Miriam Nabavizadeh, Ali Lepore, Natasha Palmer, Joshua Porras, Antonio Albrecht, Jake Anazodo, Udunna Aboian, Mariam Calabrese, Evan Rudie, Jeffrey David Linguraru, Marius George Iglesias, Juan Eugenio Van Leemput, Koen Bakas, Spyridon Wiestler, Benedikt Ezhov, Ivan Piraud, Marie Menze, Bjoern H. Helmholtz AI Germany Technical University of Munich Germany Helmholtz Munich Germany Klinikum Rechts der Isar Munich Germany SETLabs Research GmbH Healthcare Technologies Munich Germany Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Institute of Clinical Neuroimmunology Faculty of Medicine Ludwig Maximilian University Munich Germany University of Zurich Switzerland Helmholtz Research Center Munich Germany Imperial College London United Kingdom Icometrix Leuven Belgium Department of Oral and Maxillofacial Surgery Radboudumc Netherlands Department of Artificial Intelligence Radboud University Netherlands MLCommons University of Nottingham Ningbo China Ningbo315100 China Center for medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland Old Dominion University NorfolkVA23529 United States Lab Crestview Radiology Lagos Nigeria Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Yale University New HavenCT United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Duke University Medical Center Department of Radiation Oncology United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Department of Neuroradiology Technical University of Munich Munich Germany Mercy Catholic Medical Center DarbyPA United States Department of Radiology Mayo Clinic RochesterMN United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 U

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Analyzing the Structure of Attention in a Transformer Language Model

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Vig, Jesse Belinkov, Yonatan Palo Alto Research Center Machine Learning and Data Science Group Interaction and Analytics Lab Palo AltoCA United States Harvard John A. Paulson School of Engineering and Applied Sciences MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

Strong gravitational lensing as a probe of dark matter

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Vegetti, S. Birrer, S. Despali, G. Fassnacht, C.D. Gilman, D. Hezaveh, Y. Levasseur, L. Perreault McKean, J.P. Powell, D.M. O’Riordan, C.M. Vernardos, G. Max Planck Institut für Astrophysik Karl-Schwarzschild-Strasse 1 Garching MünchenD-85748 Germany Department of Physics and Astronomy Stony Brook University Stony BrookNY11794 United States Dipartimento di Fisica e Astronomia "A. Righi" Alma Mater Studiorum Università di Bologna Via Piero Gobetti 93/2 BolognaI-40129 Italy Institut für Theoretische Astrophysik Zentrum für Astronomie Heidelberg Universität Albert-Ueberle-Str. 2 Heidelberg69120 Germany Dept. of Physics and Astronomy University of California Davis 1 Shields Ave. DavisCA95616 United States Department of Astronomy and Astrophysics University of Toronto TorontoONM5S 3H4 Canada Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Kapteyn Astronomical Institute University of Groningen PO Box 800 AV Groningen9700 Netherlands ASTRON Netherlands Institute for Radio Astronomy PO Box 2 AA Dwingeloo7990 Netherlands Observatoire de Sauverny Versoix1290 Switzerland

Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution followup observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies