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" Computational diffusion MRI : "
Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M.W. Tax, editors.
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BL
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| Record Number
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860694
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| Main Entry
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Workshop on Computational Diffusion MRI(2018 :, Granada, Spain)
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| Title & Author
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Computational diffusion MRI : : International MICCAI Workshop, Granada, Spain, September 2018 /\ Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M.W. Tax, editors.
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| Publication Statement
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Cham, Switzerland :: Springer,, [2019]
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| Series Statement
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Mathematics and visualization
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| Page. NO
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1 online resource :: illustrations (some color)
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| ISBN
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3030058301
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: 303005831X
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: 3030058328
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: 9783030058302
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: 9783030058319
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: 9783030058326
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9783030058302
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Part I Diffusion MRI signal acquisition and processing strategies -- Part II Machine learning for diffusion MRI -- Part III Diffusion MRI signal harmonisation -- Part IV Diffusion MRI outside the brain and clinical applications -- Part V Tractography and connectivity mapping -- Index.
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| Abstract
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This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI'18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as harmonisation and frontline applications in research and clinical practice. The respective papers constitute invited works from high-profile researchers with a specific focus on three topics that are now gaining momentum within the diffusion MRI community: i) machine learning for diffusion MRI; ii) diffusion MRI outside the brain (e.g. in the placenta); and iii) diffusion MRI for multimodal imaging. The book shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. It includes rigorous mathematical derivations, a wealth of full-colour visualisations, and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics alike.
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Diffusion magnetic resonance imaging, Congresses.
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Diffusion magnetic resonance imaging.
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HEALTH FITNESS-- Diseases-- General.
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MEDICAL-- Clinical Medicine.
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MEDICAL-- Diseases.
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MEDICAL-- Evidence-Based Medicine.
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MEDICAL-- Internal Medicine.
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| Dewey Classification
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616.07/548
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| LC Classification
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RC78.7.N83W67 2018
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| Added Entry
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Bonet-Carne, Elisenda
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Grussu, Francesco
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Ning, Lipeng
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Sepehrband, Farshid
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Tax, Chantal M. W.
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International Conference on Medical Image Computing and Computer-Assisted Intervention(21st :2018 :, Granada, Spain)
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