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arXiv 2020年

作者： Chowdhury, Muhammad E.H. Rahman, Tawsifur Khandakar, Amith Al-Madeed, Somaya Zughaier, Susu M. Doi, Suhail A.R. Hassen, Hanadi Islam, Mohammad T. Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Biomedical Physics & Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine QU Health Qatar University Doha2713 Qatar Department of Population Medicine College of Medicine QU Health Qatar University Doha2713 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia Bangi Selangor43600 Malaysia

Background COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Methods In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Results Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at hospital admission were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate- and high-risk groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability less than 5%, 5% to 50%, and above 50%, respectively. Conclusions The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 — Ex vivo imaging: added value and acquisition

arXiv

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arXiv 2022年

作者： Schilling, Kurt G. Grussu, Francesco Ianus, Andrada Hansen, Brian Howard, Amy F.D. Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Allan Johnson, G. Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Re

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio (SNR) and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY.

关键词： Image acquisition

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Assessing the performance of novel and sustainable electrochemical sensor based on cobalt sulfide/upcycled reduced graphene oxide from plastic waste

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Electrochemistry Communications 2025年 177卷

作者： Arafat Toghan N. Roushdy Sami A. Al-Hussain Mohamed S. Elnouby Samah M. Yousef A.A.M. Farag M. Elsayed Youssef Noha A. Elessawy Chemistry Department College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11623 Saudi Arabia Electronics Materials Department Advanced Technology and New Materials Research Institute City of Scientific Research and Technological Applications (SRTA-City) New Borg El-Arab City Alexandria 21934 Egypt Composite and Nanostructured Materials Research Department Advanced Technology and New Materials Research Institute City of Scientific Research and Technological Applications (SRTA-City) New Borg El-Arab City Alexandria 21934 Egypt Basic Science Department Higher Institute of Engineering and Technology New Borg El-Arab City Alexandria Egypt Thin Film Laboratory Physics Department Faculty of Education Ain Shams University Heliopolis Roxy Cairo 11757 Egypt Computer Based Engineering Applications Dept. Informatics Research Institute City of Scientific Research and Technological Applications (SRTA-City) New Borg El-Arab City Alexandria 21934 Egypt

Developing electrochemical sensors that are both highly sensitive and environmentally sustainable is a pressing need in modern healthcare and environmental analysis. In this study, a cobalt sulfide/reduced graphene oxide (CoS/rGO) nanocomposite was synthesized via a straightforward, low-cost method that integrates thiourea and cobalt acetate to form CoS, with rGO derived from recycled plastic waste. The structural and electrochemical properties of the composites were systematically investigated using cyclic voltammetry (CV) and linear sweep voltammetry (LSV). The addition of rGO, varied from 0 to 50 wt%, notably enhanced the electrical conductivity and surface activity of the sensing interface. Among the tested formulations, the composite containing 40 wt% rGO exhibited the highest performance, achieving a sensitivity of 12.4 μA mM −1 cm −2 and a detection limit of 0.2 μM, which is approximately 7.5 times lower than that of pristine CoS. Kinetic analysis confirmed that the sensing mechanism follows a pseudo-second-order model, indicative of a chemisorption-driven interaction between paracetamol molecules and the sensor surface. The sensor displayed excellent operational stability over 100 consecutive cycles and high repeatability with a relative standard deviation below 2.5 %. This work demonstrates a novel, green strategy for sensor fabrication that effectively combines electronic functionality with environmental sustainability, making the CoS/rGO nanocomposite a viable platform for next-generation sensing technologies.

关键词： CoS NPs Plastic waste recycling Reduced graphene oxide Paracetamol Sensitivity-kinetic assessments

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Common Limitations of Image Processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Tethered Balloon Technology for Green Communication in Smart Cities and Healthy Environment 1

Tethered Balloon Technology for Green Communication in Smart...

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1st International Conference of Intelligent Computing and engineering, ICOICE 2019

作者： Alsamhi, S.H. Mohsen Aljabali, Hasan Najim, Mohd Srivastava, Ashutosh Samar Ansari, Mohd Zhao, Liang Nguyen Van, Sau Gupta, S.K. Alammari, Amr A. Hatem Saber, Akram Hebah, Mohammed Y.A.M. Ahmed Abdullah Alasali, Marwan IBB University Yemen and Aligarh Muslim University Dept. of Electronics Engineering Ibb Yemen Aligarh Muslim University Department of Electrical Engineering Aligarh India University of Jaddah Department of Electrical and Electronics Engineering Jaddah Saudi Arabia Department of Electrical Engineering Varanasi India Software Research Institute Athlone Institute of Technology Athlone Ireland School of Computer Science Shenyang Aerospace University Shenyang China Biomedical of Information Technology Shenzhen Insitutes of Advanced Technology UCAS Shenzhen China School of Electronics and Communication Engineering Shri Mata Vaishno Devi University Katra India

ISBN: (纸本)9781728144870

The development and adopting of advanced communication technologies provide mobile users more convenience to connect any wireless network anytime and anywhere. Therefore, a large number of base stations (BS) are demanded keeping users connectivity, enhancing network capacity, and guarantee a sustained users Quality of Experiences (QoS). However, increasing the number of BS leads to an increase in the ecological ad radiation hazards. In order to green communication, many factors should be taken into consideration, i.e., saving energy, guarantee QoS, and reducing pollution hazards. Therefore, we propose tethered balloon technology that can replace a large number of BS and reduce ecological and radiation hazards due to its high altitude and feasible green and healthy broadband communication. The main contribution of this paper is to deploy tethered balloon technology at different altitude and measure the power density. Furthermore, we evaluate the measurement of power density from different height of tethered balloon comparison with traditional wireless communication technologies. The simulation results showed that tethered balloon technology can deliver green communication effectively and efficiently without any hazardous impacts. © 2019 IEEE.

关键词： Smart city

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Severity classification of obstructive sleep apnea using only heart rate variability measures with an ensemble classifier

Severity classification of obstructive sleep apnea using onl...

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2018 IEEE EMBS International Conference on biomedical and Health informatics, BHI 2018

作者： Rahman, Md Juber Mahajan, Ruhi Morshed, Bashir I. Electrical and Computer Engineering Department University of Memphis MemphisTN United States Dept. of Pediatrics UTHSC Oak Ridge National Laboratory Center for Biomedical Informatics University of Tennessee Health Science Center MemphisTN United States

ISBN: (纸本)9781538624050

Identifying non-polysomnographic predictors of sleep apnea severity is an on-going challenge in the sleep research. In this study, we detect the severity of obstructive sleep apnea (OSA) condition using features derived from a single lead electrocardiogram (ECG) signal. We have explored 17 time domain, frequency domain, and nonlinear Heart Rate Variability (HRV) features to distinguish subjects in the severe and non-severe OSA groups. An optimal set of nine features including Poincare plot based metrics was found to provide significant discriminative information (p-value © 2018 IEEE.

关键词： Sleep research

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Automated Assessment of Pain Intensity Based on EEG Signal Analysis

Automated Assessment of Pain Intensity Based on EEG Signal A...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Panagiotis A. Bonotis Dimosthenis C. Tsouros Panagiotis N. Smyrlis Alexandros T. Tzallas Nikolaos Giannakeas Evripidis Glavas Markos G. Tsipouras Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunication University of Ioannina Arta Greece UNIVEYE IKE Science and Technology Park of Epirus Ioannina Greece Dept. of Electrical and Computer Engineering Univ. of Western Macedonia Kozani Greece

Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band's energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.

关键词： Protocols Pain Forestry Tools Electroencephalography Mobile handsets Signal analysis

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P18.04.B SINGLE CELL TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISTINCT INTRATUMORAL SUBCLONES IN MENINGIOMA

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii122–ii122页

作者： Blume, C Tang, Y Dogan, H Ritter, M Dentro, S von Deimling, A Gerstung, M Schlesner, M Sahm, F Maas, S L N University Hospital Heidelberg Dept. of Neuropathology Heidelberg Germany German Cancer Research Center (DKFZ) German Consortium for Translational Cancer Research (DKTK) Clinical Cooperation Unit Neuropathology Heidelberg Germany Division of AI in Oncology German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton United Kingdom Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany Department of Pathology Leiden University Medical Center Leiden Netherlands Department of Pathology Erasmus Medical Center Rotterdam Netherlands

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Modulated clustering using integrated rough sets and scatter search attribute reduction 18

Modulated clustering using integrated rough sets and scatter...

引用

2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Hedar, Abdel-Rahman Ibrahim, Abdel-Monem M. Sewisy, Adel A. Abdel-Hakim, Alaa E. Dept. of Computer Science Faculty of Comp. and Informatics Assiut University Assiut71526 Egypt Dept. of Mathematics Faculty of Science Al-Azhar University Assiut71524 Egypt Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Dept. of Computer Science Jamoum University College Umm Al-Qura University Makkah Saudi Arabia

ISBN: (纸本)9781450357647

Feature selection is an essential problem in pattern classification systems. The entire performance of the classifier is highly affected by the quality of the selected features. In this paper, we address this problem by integrating feature selection with the clustering process. A novel feature-modulated classification algorithm is proposed to improve the classification accuracy. We use a rough sets approach for feature selection based on a scatter search meta-heuristic scheme. The proposed approach sifts a compact subset of characterizing features in multi-class systems according to the clustering performance. To verify the effectiveness of our method, experimental comparisons are carried out on eleven benchmark datasets using two typical classifiers. The results indicate that the proposed method has a remarkable ability to generate effective reduced-size subsets of salient features while yielding significant classification accuracy. © 2018 ACM

关键词： Feature Selection

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Roadmap on wavefront shaping and deep imaging in complex media

arXiv

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arXiv 2021年

作者： Gigan, Sylvain Katz, Ori de Aguiar, Hilton B. Andresen, Esben Ravn Aubry, Alexandre Bertolotti, Jacopo Bossy, Emmanuel Bouchet, Dorian Brake, Joshua Brasselet, Sophie Bromberg, Yaron Cao, Hui Chaigne, Thomas Cheng, Zhongtao Choi, Wonshik Čižmár, Tomáš Cui, Meng Curtis, Vincent R. Defienne, Hugo Hofer, Matthias Horisaki, Ryoichi Horstmeyer, Roarke Ji, Na LaViolette, Aaron K. Mertz, Jerome Moser, Christophe Mosk, Allard P. Pégard, Nicolas C. Piestun, Rafael Popoff, Sebastien Phillips, David B. Psaltis, Demetri Rahmani, Babak Rigneault, Hervé Rotter, Stefan Tian, Lei Vellekoop, Ivo M. Waller, Laura Wang, Lihong Weber, Timothy Xiao, Sheng Xu, Chris Yamilov, Alexey Yang, Changhuei Yılmaz, Hasan Cornell University United States University of California Berkeley United States Department of Biomedical Engineering Boston University United States Laboratoire Kastler Brossel ENS-Université PSL CNRS Sorbonne Université Collège de France 24 rue Lhomond Paris75005 France Univ. Grenoble Alpes CNRS LIPhy Grenoble38000 France Aix Marseille Univ. CNRS Centrale Marseille Institut Fresnel Marseille France Institute for Basic Science Department of Physics Korea University Korea Republic of Institut Langevin ESPCI PSL University CNRS Paris France Aix Marseille Univ CNRS Centrale Marseille Institut Fresnel MarseilleF-13013 France School of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Leibniz Institute of Photonic Technology Albert-Einstein-Straße 9 Jena07745 Germany Laboratory of Applied Photonics Devices School of Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Laboratory of Optics School of Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Université de Lille CNRS UMR 8523 PhLAM Laboratoire de Physique des Lasers Atomes et Molécules LilleF-59000 France Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Applied Physical Sciences UNC Chapel Hill United States Department of Electrical Engineering and Computer Sciences UC Berkeley United States Department of Biomedical Engineering UNC Chapel Hill United States UNC Neuroscience Centre UNC Chapel Hill United States Department of Applied Physics Hebrew University of Jerusalem Jerusalem Israel Department of Science and Technology University of Twente Enschede Netherlands University of Tokyo Japan Institute of Scientific Instruments of the Czech Academy of Sciences Královopolská 147 Brno612 64 Czech Republic Dept. of Applied Physics Yale University United States Dept. of Physics Missouri University of Science and Technology United States Bi

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at dept. in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead. Copyright © 2021, The Authors. All rights reserved.

关键词： Multimode fibers

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