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E-grāmata: Computational Diffusion MRI: MICCAI Workshop, Quebec, Canada, September 2017

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  • Formāts: PDF+DRM
  • Sērija : Mathematics and Visualization
  • Izdošanas datums: 02-Apr-2018
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319738390
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  • Formāts: PDF+DRM
  • Sērija : Mathematics and Visualization
  • Izdošanas datums: 02-Apr-2018
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319738390
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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 Québec, 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 Apparent Propagator
Fields Using Dynamic Programming on a Minimum Spanning Tree by K“evin
Ginsburger et al.- Diffusion Orientation Histograms (DOH) for Diffusion
Weighted Image Analysis by Laurent Chauvin et al.- Part III Tractography and
Connectivity: Learning aSingle Step of Streamline Tractography Based on
Neural Networks by Daniel Jörgens et al.- Probabilistic Tractography for
Complex Fiber Orientations with Automatic Model Selection by Edwin Versteeg
et al.- Bundle-Specific Tractography by Francois Rheault et al.- A Sheet
Probability Index from Diffusion Tensor Imaging by Michael Ankele et al.-
Recovering Missing Connections in Diffusion Weighted MRI Using Matrix
Completion by Chendi Wang et al.- Brain Parcellation and Connectivity Mapping
Using Wasserstein Geometry by Hamza Farooq et al.- Exploiting Machine
Learning Principles for Assessing the Fingerprinting Potential of
Connectivity Features by Silvia Obertino et al.- Part IV Clinical
Applications: Fiber-Flux Diffusion Density for White Matter Tracts Analysis:
Application to Mild Anomalies Localization in Contact Sports Players by Itay
Benou et al.- Longitudinal Analysis Framework of DWI Data for Reconstructing
Structural Brain Networks with Application to MultipleSclerosis by Thalis
Charalambous et al.- Multi-Modal Analysis of Genetically-Related Subjects
Using SIFT Descriptors in Brain MRI by Kuldeep Kumar et al.- VERDICT Prostate
Parameter Estimation with AMICO by Elisenda Bonet-Carne et al.
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