Computational Diffusion MRI

Muge Karaman

ISBN 10: 3031472918 / ISBN 13: 9783031472916

Published by Springer Nature Switzerland, Springer Nature Switzerland Feb 2024, 2024

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023.The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms. 216 pp. Englisch.

Seller Inventory # 9783031472916

Bibliographic Details

• Title: Computational Diffusion MRI
• Publisher: Springer Nature Switzerland, Springer Nature Switzerland Feb 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 3031472918
• ISBN 13: 9783031472916
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 335 grams

About this title

Synopsis

This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023.

The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.