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arXiv 2021年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Moridian, Parisa Rezaei, Mitra Alizadehsani, Roohallah Khozeimeh, Fahime Gorriz, Juan Manuel Heras, Jónathan Panahiazar, Maryam Nahavandi, Saeid Acharya, U. Rajendra K. N. Toosi University of Technology Tehran Iran Faculty of Engineering Mashhad Branch Islamic Azad University Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Electrical and Computer Engineering Dept. Tarbiat Modares University Tehran Iran Deakin University Geelong Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom Department of Mathematics and Computer Science University of La Rioja La Rioja Spain University of California San Francisco San FranciscoCA United States Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Dept. of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far;among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error;on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

arXiv

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arXiv 2021年

作者： Jelescu, Ileana O. de Skowronski, Alexandre Geffroy, Françoise Palombo, Marco Novikov, Dmitry S. CIBM Center for Biomedical Imaging Animal Imaging and Technology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Lausanne Switzerland Lausanne Switzerland NeuroSpin CEA Saclay France School of Psychology Cardiff University Cardiff United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Centre for Medical Image Computing Dept. of Computer Science University College London London United Kingdom Center for Biomedical Imaging Department of Radiology New York University School of Medicine New YorkNY United States

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space;non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder;and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the investigated diffusion time window (~10-45 ms) show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisitions protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This sugg

关键词： Magnetic resonance imaging

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette 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 Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems 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 Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal 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 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 Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

SSRN

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SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

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Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

arXiv

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arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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MobiCardio: A clinical-grade mobile health system for cardiovascular disease management 7

MobiCardio: A clinical-grade mobile health system for cardio...

引用

7th IEEE International Conference on Healthcare informatics, ICHI 2019

作者： Li, Peiyao Cao, Desen Zhang, Zhengbo Yang, Zhicheng Yan, Wei Yan, Muyang He, Maoqing Yuan, Qian Lan, Ke Zheng, Jiewen Liu, Tongbo Chinese PLA General Hospital Dept. of Biomedical Engineering Beijing China University of California Dept. of Computer Science DavisCA United States Chinese PLA General Hospital Dept. of Hyperbaric Oxygen Therapy Beijing China Beijing SensEcho Science Technology Co. Ltd. Beijing China Chinese PLA General Hospital Dept. of Information Technology Beijing China Department of Biomedical Engineering and Medical Device Research and Development Evaluation Center China

ISBN: (纸本)9781538691380

Cardiovascular disease (CVD) is the leading death factor in China. Mobile Health (mHealth) has been proven as a promising solution for CVD management. However, mHealth for CVD management in Chinese healthcare system has not been widely adopted. In this paper, we introduce our holistic CVD management mHealth system, MobiCardio, which enables the efficient management of CVD patients specialized for clinical scenarios, by the integration of multi-sensor wearable technologies, machine-learning-based sleep analytics, and electronic health records (EHRs) information. Several ongoing featured clinical validation projects are described. © 2019 IEEE.

关键词： mHealth

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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

引用

International Conference on Laser Optics

作者： L. M. Oliveira T. M. Gonç alves A. R. Botelho I. S. Martins H. F. Silva I. Carneiro S. Carvalho R. Henrique V. V. Tuchin ISEP Center of Innovation in Engineering and Industrial Technology Porto Portugal Physics Department Polytechnic of Porto - School of Engineering (ISEP) Porto Portugal Department of Electrical and Computer Engineering Porto University - School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Dept. of Pathological Cytological and Thanatological Anatomy Polytechnic of Porto - School of Health (ESS) Porto Portugal Deptartment of Pathology Santa Luzia Hospital (ULSAM) Viana do Castelo Portugal Dept. of Pat hol. and Molecular Immunology Porto University - Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control FRC &#x2018 Saratov Scientific Centre&#x2019 of the Russian Academy of Sciences Saratov Russia

ISBN: (数字)9781665466646

ISBN: (纸本)9781665466653

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans.

关键词： Rabbits Absorption Neurons Lasers Aging Pigments Optics

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Comprehensive Analysis of Non Redundant Protein Database

Research Square

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Research Square 2020年

作者： Bagheri, Hamid Dyer, Robert Severin, Andrew Rajan, Hridesh Department of Computer Science Iowa State University 226 Atanasoff Hall Ames50011 United States Dept. of Computer Science & Engineering University of Nebraska Lincoln Lincoln68588 United States Genome Informatics Facility Iowa State University 206 Science I Ames50011 United States

Background: Scientists around the world use NCBI’s non-redundant (NR) database to identify the taxonomic origin and functional annotation of their favorite protein sequences using BLAST. Unfortunately, due to the exponential growth of this database, many scientists do not have a good understanding of the contents of the NR database. There is a need for tools to explore the contents of large biological datasets, such as NR, to better understand the assumptions and limitations of the data they contain. Results: Protein sequence data, protein functional annotation, and taxonomic assignment from NCBI’s NR database were placed into a BoaG database, a domain-specific language and shared data science infrastructure for genomics, along with a CD-HIT clustering of all these protein sequences at different sequence similarity levels. We show that BoaG can efficiently perform queries on this large dataset to determine the average length of protein sequences and identify the most common taxonomic assignments and functional annotations. Using the clustering information, we also show that the non-redundant (NR) database has a considerable amount of annotation redundancy at the 95% similarity level. Conclusions: We implemented BoaG and provided a web-based interface to BoaG’s infrastructure that will help researchers to explore the dataset further. Researchers can submit queries and download the results or share them with others. Availability and implementation: The web-interface of the BoaG infrastructure can be accessed here: http://***/boag. Please use user = boag and password = boag to login. Source code and other documentation are also provided as a GitHub repository: https://***/boalang/NR_Dataset. © 2020, CC BY.

关键词： Database systems

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2020 CATARACTS Semantic Segmentation Challenge

arXiv

引用

arXiv 2021年

作者： Luengo, Imanol Grammatikopoulou, Maria Mohammadi, Rahim Walsh, Chris Nwoye, Chinedu Innocent Alapatt, Deepak Padoy, Nicolas Ni, Zhen-Liang Fan, Chen-Chen Bian, Gui-Bin Hou, Zeng-Guang Ha, Heonjin Wang, Jiacheng Wang, Haojie Guo, Dong Wang, Lu Wang, Guotai Islam, Mobarakol Giddwani, Bharat Hongliang, Ren Pissas, Theodoros Ravasio, Claudio Huber, Martin Birch, Jeremy Nunez Do Rio, Joan M. da Cruz, Lyndon Bergeles, Christos Chen, Hongyu Jia, Fucang Tomar, Nikhil Kumar Jha, Debesh Riegler, Michael A. Halvorsen, Pal Bano, Sophia Vaghela, Uddhav Hong, Jianyuan Ye, Haili Huang, Feihong Wang, Da-Han Stoyanov, Danail Digital Surgery Ltd Medtronic Ireland ICube University of Strasbourg CNRS IHU Strasbourg France Institute of Automation Chinese Academy of Sciences China Hutom Seoul Korea Republic of Department of Computer Science Xiamen University Xiamen China University of Electronic Science and Technology of China Chengdu China Dept. of Biomedical Engineering National University of Singapore Singapore Dept. of Electronics and Telecoms. Engineering National Institute of Technology Raipur India School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom University College London London United Kingdom Institute of Ophthalmology University College London London United Kingdom Moorfields Eye Hospital London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academyof Sciences China SimulaMet UiT The Arctic University of Norway Oslo Metropolitan University Norway Department of Computer Science University College London United Kingdom School of Computer and Information Engineering Xiamen University of Technology China

Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmentation Challenge, which was a sub-challenge of the 2020 MICCAI Endoscopic Vision (EndoVis) Challenge, presented three sub-tasks to assess participating solutions on anatomical structure and instrument segmentation. Their performance was assessed on a hidden test set of 531 images from 10 videos of the CATARACTS test set. © 2021, CC BY-NC-ND.

关键词： Semantics

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

引用

arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian 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 van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio 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. Johnson, G. Allan 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 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 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 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 Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. 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 that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a 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 concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. 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. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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