Editors:
Contributions on new important topics that are gaining momentum within the diffusion MRI community
Details new computational methods and estimation techniques for microstructure imaging and brain connectivity mapping
Features papers presented at the 2018 MICCAI Workshop on Computational Diffusion MRI (CDMRI’18)
Part of the book series: Mathematics and Visualization (MATHVISUAL)
Conference series link(s): MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Conference proceedings info: MICCAI 2019.
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Table of contents (31 papers)
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Front Matter
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Diffusion MRI Signal Acquisition and Processing Strategies
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Front Matter
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Machine Learning for Diffusion MRI
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Front Matter
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Diffusion MRI Signal Harmonization
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Front Matter
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Other Volumes
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Computational Diffusion MRI
About this book
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.
Keywords
- diffusion MRI
- computational techniques
- medical image computing
- medical visualization
- image acquisition
- image registration
- image reconstruction
- image analysis
- image and signal processing
- machine learning
- brain MRI
- neuroimaging
- connectomics
- fibre tractography
- body MRI
- microsturcture imaging
- signal modelling
- parameter estimation
Editors and Affiliations
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Centre for Medical Image Computing, University College London, London, UK
Elisenda Bonet-Carne
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Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
Francesco Grussu
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Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
Lipeng Ning
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Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA
Farshid Sepehrband
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Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
Chantal M. W. Tax
Bibliographic Information
Book Title: Computational Diffusion MRI
Book Subtitle: International MICCAI Workshop, Granada, Spain, September 2018
Editors: Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M. W. Tax
Series Title: Mathematics and Visualization
DOI: https://doi.org/10.1007/978-3-030-05831-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-05830-2Published: 07 June 2019
eBook ISBN: 978-3-030-05831-9Published: 17 May 2019
Series ISSN: 1612-3786
Series E-ISSN: 2197-666X
Edition Number: 1
Number of Pages: XII, 390
Number of Illustrations: 17 b/w illustrations, 109 illustrations in colour
Topics: Mathematical and Computational Biology, Numeric Computing, Math Applications in Computer Science, Image Processing and Computer Vision, Simulation and Modeling, Artificial Intelligence