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Computational Diffusion MRI

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Cover of 'Computational Diffusion MRI'

Table of Contents

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    Book Overview
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    Chapter 1 Estimating Tissue Microstructure Using Diffusion-Weighted Magnetic Resonance Spectroscopy of Brain Metabolites
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    Chapter 2 ( k , q )-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior
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    Chapter 3 Spatio-Temporal dMRI Acquisition Design: Reducing the Number of qτ Samples Through a Relaxed Probabilistic Model
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    Chapter 4 A Generalized SMT-Based Framework for Diffusion MRI Microstructural Model Estimation
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    Chapter 5 Diffusion Specific Segmentation: Skull Stripping with Diffusion MRI Data Alone
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    Chapter 6 Diffeomorphic Registration of Diffusion Mean Apparent Propagator Fields Using Dynamic Programming on a Minimum Spanning Tree
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    Chapter 7 Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis
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    Chapter 8 Learning a Single Step of Streamline Tractography Based on Neural Networks
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    Chapter 9 Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection
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    Chapter 10 Bundle-Specific Tractography
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    Chapter 11 A Sheet Probability Index from Diffusion Tensor Imaging
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    Chapter 12 Recovering Missing Connections in Diffusion Weighted MRI Using Matrix Completion
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    Chapter 13 Brain Parcellation and Connectivity Mapping Using Wasserstein Geometry
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    Chapter 14 Exploiting Machine Learning Principles for Assessing the Fingerprinting Potential of Connectivity Features
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    Chapter 15 Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
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    Chapter 16 Longitudinal Analysis Framework of DWI Data for Reconstructing Structural Brain Networks with Application to Multiple Sclerosis
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    Chapter 17 Multi-Modal Analysis of Genetically-Related Subjects Using SIFT Descriptors in Brain MRI
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    Chapter 18 VERDICT Prostate Parameter Estimation with AMICO
Overall attention for this book and its chapters
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Title
Computational Diffusion MRI
Published by
Springer International Publishing, May 2018
DOI 10.1007/978-3-319-73839-0
ISBNs
978-3-31-973838-3, 978-3-31-973839-0
Editors

Kaden, Enrico, Grussu, Francesco, Ning, Lipeng, Tax, Chantal M. W., Veraart, Jelle

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 4%
Student > Master 1 1%
Unknown 66 94%
Readers by discipline Count As %
Engineering 2 3%
Computer Science 1 1%
Unknown 67 96%