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Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019: Mathematics and Visualization

Editat de Elisenda Bonet-Carne, Jana Hutter, Marco Palombo, Marco Pizzolato, Farshid Sepehrband, Fan Zhang
en Limba Engleză Paperback – 7 noi 2021
This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI 2019), held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), which took place in Shenzhen, China on October 17, 2019.
This book presents the latest advances in the rapidly expanding field of diffusion MRI. It 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 about computational techniques in diffusion MRI. The book includes rigorous mathematical derivations, a wealth of rich, full-colour visualisations and extensive 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. Readers will find contributions covering 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 diffusion-relaxometry and frontline applications in research and clinical practice. This edition includes invited works from high-profile researchers with a specific focus on three new and important topics that are gaining momentum within the diffusion MRI community, including diffusion MRI signal acquisition and processing strategies, machine learning for diffusion MRI, and diffusion MRI outside the brain and clinical applications.

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Specificații

ISBN-13: 9783030528959
ISBN-10: 3030528952
Ilustrații: XI, 210 p. 78 illus., 64 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.32 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Mathematics and Visualization

Locul publicării:Cham, Switzerland

Cuprins

Diffusion MRI signal acquisition and processing strategies.- Machine learning for diffusion MRI.- Combined diffusion-relaxometry MRI.

Textul de pe ultima copertă

This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI 2019), held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), which took place in Shenzhen, China on October 17, 2019.
This book presents the latest advances in the rapidly expanding field of diffusion MRI. It 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 about computational techniques in diffusion MRI. The book includes rigorous mathematical derivations, a wealth of rich, full-colour visualisations and extensive 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. Readers will find contributions covering 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 diffusion-relaxometry and frontline applications in research and clinical practice. This edition includes invited works from high-profile researchers with a specific focus on three new and important topics that are gaining momentum within the diffusion MRI community, including diffusion MRI signal acquisition and processing strategies, machine learning for diffusion MRI, and diffusion MRI outside the brain and clinical applications.




Caracteristici

Contributions on new important topics that are gaining momentum within the diffusion MRI community Careful mathematical derivations and large number of rich full-color visualizations Biologically or clinically relevant results