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Computational Diffusion MRI and Brain Connectivity: MICCAI Workshops, Nagoya, Japan, September 22nd, 2013 Softcover reprint of the original 1st ed. 2014 Edition
Author(s): Thomas Schultz (Editor), Gemma Nedjati-Gilani (Editor), Archana Venkataraman (Editor), Lauren O'Donnell (Editor), Eleftheria Panagiotaki (Editor)
- Publisher: Springer
- Publication Date: August 23, 2016
- Edition: Softcover reprint of the original 1st ed. 2014
- Language: English
- Print length: 269 pages
- ISBN-10: 3319376845
- ISBN-13: 9783319376844
Book Description
This volume contains the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI’13) and Mathematical Methods from Brain Connectivity (MMBC’13), held under the auspices of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, which took place in Nagoya, Japan, September 2013.
Inside, readers will find contributions ranging from mathematical foundations and novel methods for the validation of inferring large-scale connectivity from neuroimaging data to the statistical analysis of the data, accelerated methods for data acquisition, and the most recent developments on mathematical diffusion modeling.
This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity as well as offers new perspectives and insights on current research challenges for those currently in the field. It will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.
Editorial Reviews
From the Back Cover
This volume contains the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI’13) and Mathematical Methods from Brain Connectivity (MMBC’13), held under the auspices of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, which took place in Nagoya, Japan, September 2013.
Inside, readers will find contributions ranging from mathematical foundations and novel methods for the validation of inferring large-scale connectivity from neuroimaging data to the statistical analysis of the data, accelerated methods for data acquisition, and the most recent developments on mathematical diffusion modeling.
This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity as well as offers new perspectives and insights on current research challenges for those currently in the field. It will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.
