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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 Vector Weights and Dual Graphs: An Emphasis on Connections in Brain Network Analysis
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    Chapter 2 Rich Club Network Analysis Shows Distinct Patterns of Disruption in Frontotemporal Dementia and Alzheimer’s Disease
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    Chapter 3 Parcellation-Independent Multi-Scale Framework for Brain Network Analysis
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    Chapter 4 Computational Diffusion MRI
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    Chapter 5 The Added Value of Diffusion Tensor Imaging for Automated White Matter Hyperintensity Segmentation
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    Chapter 6 Algebraic Connectivity of Brain Networks Shows Patterns of Segregation Leading to Reduced Network Robustness in Alzheimer’s Disease
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    Chapter 7 Diffusion-Map: A Novel Visualizing Biomarker for Diffusion Tensor Imaging of Human Brain White Matter
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    Chapter 8 A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis
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    Chapter 9 Computational Diffusion MRI
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    Chapter 10 Fiber Bundle Segmentation Using Spectral Embedding and Supervised Learning
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    Chapter 11 Atlas-Guided Global Tractography: Imposing a Prior on the Local Track Orientation
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    Chapter 12 Magnitude and Complex Based Diffusion Signal Reconstruction
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    Chapter 13 Diffusion Propagator Estimation Using Gaussians Scattered in q-Space
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    Chapter 14 An Analytical 3D Laplacian Regularized SHORE Basis and Its Impact on EAP Reconstruction and Microstructure Recovery
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    Chapter 15 Motion Is Inevitable: The Impact of Motion Correction Schemes on HARDI Reconstructions
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    Chapter 16 Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate
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    Chapter 17 Bilateral Filtering of Multiple Fiber Orientations in Diffusion MRI
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    Chapter 18 Dictionary Based Super-Resolution for Diffusion MRI
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Title
Computational Diffusion MRI
Published by
Springer, Cham, January 2014
DOI 10.1007/978-3-319-11182-7
ISBNs
978-3-31-911181-0, 978-3-31-911182-7
Editors

Lauren O'Donnell, Gemma Nedjati-Gilani, Yogesh Rathi, Marco Reisert, Torben Schneider

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 5%
Professor 2 5%
Student > Ph. D. Student 2 5%
Student > Master 2 5%
Researcher 1 3%
Other 1 3%
Unknown 27 73%
Readers by discipline Count As %
Engineering 3 8%
Neuroscience 2 5%
Business, Management and Accounting 1 3%
Computer Science 1 3%
Unknown 30 81%