---

＜電子ブック＞
Computational Diffusion MRI : International MICCAI Workshop, Granada, Spain, September 2018 / edited by Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M. W. Tax
(Mathematics and Visualization. ISSN:2197666X)

版	1st ed. 2019.
出版者	Cham : Springer International Publishing : Imprint: Springer
出版年	2019
本文言語	英語
大きさ	XII, 390 p. 126 illus., 109 illus. in color : online resource
著者標目	Bonet-Carne, Elisenda editor Grussu, Francesco editor Ning, Lipeng editor Sepehrband, Farshid editor Tax, Chantal M. W editor SpringerLink (Online service)
件　名	LCSH:Biomathematics LCSH:Numerical analysis LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Computer vision LCSH:Computer simulation LCSH:Artificial intelligence FREE:Mathematical and Computational Biology FREE:Numerical Analysis FREE:Mathematical Applications in Computer Science FREE:Computer Vision FREE:Computer Modelling FREE:Artificial Intelligence
一般注記	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 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. HTTP:URL=https://doi.org/10.1007/978-3-030-05831-9

 類似資料