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Link to original content: https://link.springer.com/book/10.1007/978-3-319-02475-2
Computational Diffusion MRI and Brain Connectivity: MICCAI Workshops, Nagoya, Japan, September 22nd, 2013 | SpringerLink
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Computational Diffusion MRI and Brain Connectivity

MICCAI Workshops, Nagoya, Japan, September 22nd, 2013

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Thomas Schultz

    1. University of Bonn, Bonn, Germany

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  2. Gemma Nedjati-Gilani

    1. Dept. of Computer Science, University College London Centre for Medical Image Computing, London, United Kingdom

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    You can also search for this editor in PubMed   Google Scholar

  3. Archana Venkataraman

    1. MIT CSAIL, Cambridge, USA

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  4. Lauren O'Donnell

    1. Brigham and Women's Hospital Depts of Radiology and Neurosurgery, Boston, USA

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  5. Eleftheria Panagiotaki

    1. Dept. of Computer Science, University College London Centre for Medical Image Computing, London, United Kingdom

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  • Offers a snapshot of the latest developments on a broad range of topics within the very active and growing fields of diffusion MR and brain connectivity
  • Features the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI’13) and Mathematical Methods from Brain Connectivity (MMBC’13)
  • Contributions include both careful mathematical derivations and a large number of rich full-color visualizations
  • Leading experts in the fields on the program committees
  • Includes supplementary material: sn.pub/extras

Part of the book series: Mathematics and Visualization (MATHVISUAL)

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About this book

This volume contains the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI’13) and Mathematical Methods from Brain Connectivity (MMBC’13), held under the auspices of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, which took place in Nagoya, Japan, September 2013.

Inside, readers will find contributions ranging from mathematical foundations and novel methods for the validation of inferring large-scale connectivity from neuroimaging data to the statistical analysis of the data, accelerated methods for data acquisition, and the most recent developments on mathematical diffusion modeling.

This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity as well as offers new perspectives and insights on current research challenges for those currently in the field. It will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.

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Keywords

Table of contents (22 papers)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Acquisition of Diffusion MRI

    1. Front Matter

      Pages 1-1
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    2. Comparing Simultaneous Multi-slice Diffusion Acquisitions

      • Yogesh Rathi, Borjan Gagoski, Kawin Setsompop, P. Ellen Grant, C.-F. Westin
      Pages 3-11

    3. Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI

      • Bryce Wilkins, Namgyun Lee, Vidya Rajagopalan, Meng Law, Natasha Leporé
      Pages 13-24

    4. Model-Based Super-Resolution of Diffusion MRI

      • Alexandra Tobisch, Peter F. Neher, Matthew C. Rowe, Klaus H. Maier-Hein, Hui Zhang
      Pages 25-34

    5. A Quantitative Evaluation of Errors Induced by Reduced Field-of-View in Diffusion Tensor Imaging

      • Jan Hering, Ivo Wolf, Hans-Peter Meinzer, Bram Stieltjes, Klaus H. Maier-Hein
      Pages 35-44

  3. Diffusion MRI Modeling

    1. Front Matter

      Pages 45-45
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    2. The Diffusion Dictionary in the Human Brain Is Short: Rotation Invariant Learning of Basis Functions

      • Marco Reisert, Henrik Skibbe, Valerij G. Kiselev
      Pages 47-55

    3. Diffusion Propagator Estimation Using Radial Basis Functions

      • Yogesh Rathi, Marc Niethammer, Frederik Laun, Kawin Setsompop, Oleg Michailovich, P. Ellen Grant et al.
      Pages 57-66

    4. A Framework for ODF Inference by Using Fiber Tract Adaptive MPG Selection

      • Hidekata Hontani, Kazunari Iwamoto, Yoshitaka Masutani
      Pages 67-79

    5. Non-negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation

      • Jian Cheng, Rachid Deriche, Tianzi Jiang, Dinggang Shen, Pew-Thian Yap
      Pages 81-93

  4. Tractography

    1. Front Matter

      Pages 95-95
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    2. A Novel Riemannian Metric for Geodesic Tractography in DTI

      • Andrea Fuster, Antonio Tristan-Vega, Tom Dela Haije, Carl-Fredrik Westin, Luc Florack
      Pages 97-104

    3. Fiberfox: An Extensible System for Generating Realistic White Matter Software Phantoms

      • Peter F. Neher, Frederik B. Laun, Bram Stieltjes, Klaus H. Maier-Hein
      Pages 105-113

    4. Choosing a Tractography Algorithm: On the Effects of Measurement Noise

      • Andre Reichenbach, Mario Hlawitschka, Marc Tittgemeyer, Gerik Scheuermann
      Pages 115-128

    5. Uncertainty in Tractography via Tract Confidence Regions

      • Colin J. Brown, Brian G. Booth, Ghassan Hamarneh
      Pages 129-138

    6. Estimating Uncertainty in White Matter Tractography Using Wild Non-local Bootstrap

      • Pew-Thian Yap, Hongyu An, Yasheng Chen, Dinggang Shen
      Pages 139-148

  5. Group Studies and Statistical Analysis

    1. Front Matter

      Pages 149-149
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    2. Groupwise Deformable Registration of Fiber Track Sets Using Track Orientation Distributions

      • Daan Christiaens, Thijs Dhollander, Frederik Maes, Stefan Sunaert, Paul Suetens
      Pages 151-161

    3. Groupwise Registration for Correcting Subject Motion and Eddy Current Distortions in Diffusion MRI Using a PCA Based Dissimilarity Metric

      • W. Huizinga, C. T. Metz, D. H. J. Poot, M. de Groot, W. J. Niessen, A. Leemans et al.
      Pages 163-174
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Editors and Affiliations

  • University of Bonn, Bonn, Germany

    Thomas Schultz

  • Dept. of Computer Science, University College London Centre for Medical Image Computing, London, United Kingdom

    Gemma Nedjati-Gilani, Eleftheria Panagiotaki

  • MIT CSAIL, Cambridge, USA

    Archana Venkataraman

  • Brigham and Women's Hospital Depts of Radiology and Neurosurgery, Boston, USA

    Lauren O'Donnell

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