This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find numerous 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 frontline applications in neuroscience research and clinical practice.
These proceedings contain the papers presented at the 2017 MICCAI Workshop on Computational Diffusion MRI (CDMRI17) held in Qu?bec, Canada on September 10, 2017, sharing new perspectives on the most recent research challenges for those currently working in the field, but also offering a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. This book includes rigorous mathematical derivations, a large number of rich, 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.
Part I Data Acquisition and Modeling: Estimating Tissue Microstructure using Diffusion-Weighted Magnetic Resonance Spectroscopy of Brain Metabolites by Marco Palombo.- (k, q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior by Evan Schwab et al.- Spatio-Temporal dMRI Acquisition Design: Reducing the Number of q? Samples Through a Relaxed Probabilistic Model by Patryk Filipiak et al.- A Generalized SMT-Based Framework for Diffusion MRI Microstructural Model Estimation by Mauro Zucchelli et al.- Part II Image Postprocessing: Diffusion Specific Segmentation: Skull Stripping with Diffusion MRIData Alone by Robert I. Reid et al.- Diffeomorphic Registration of Diffusion Mean Apparl³$
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