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Choosing a Tractography Algorithm: On the Effects of Measurement Noise

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Computational Diffusion MRI and Brain Connectivity

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

Abstract

Diffusion MRI tractography has evolved into a widely used, important tool within neurosciences, providing the foundation for in-vivo fiber anatomy and hence for mapping of structural connectivity in the human brain. This renders it crucially important to understand the influence of the various MRI imaging artifacts on the tractography results. In this paper, we focus on the thermal noise that is present in all MRI measurements and compare its effect on the output of several established tractography algorithms. We create a reference dataset by denoising with a Non-Local Means filter and evaluate the effect of noise added to the reference on the tractography results with a Monte-Carlo simulation. Our results indicate that among the algorithms tested, the Tensorlines approach is the most robust for tracking white matter fiber bundles and both the Tensorlines and the Bayes DTI approach are good choices for calculating gray matter structural connectivity.

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Notes

  1. 1.

    http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST

  2. 2.

    http://surfer.nmr.mgh.harvard.edu/fswiki

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Acknowledgements

This work was funded by the Leipzig University. We thank our colleague Stefan Philips for providing the implementation of Reisert’s algorithm, Mathias Goldau and Stefan Koch for proofreading and fruitful discussions, and Corina Melzer from the Max-Planck-Institute in Cologne for her feedback. We would also like to thank the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Leipzig University, 04109, Leipzig, Germany

    Andre Reichenbach, Mario Hlawitschka & Gerik Scheuermann

  2. Max-Planck-Institute for Neurological Research, 50931, Cologne, Germany

    Marc Tittgemeyer

Authors
  1. Andre Reichenbach
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  2. Mario Hlawitschka
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  3. Marc Tittgemeyer
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  4. Gerik Scheuermann
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Corresponding author

Correspondence to Andre Reichenbach .

Editor information

Editors and Affiliations

  1. University of Bonn, Bonn, Germany

    Thomas Schultz

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

    Gemma Nedjati-Gilani

  3. MIT CSAIL, Cambridge, Massachusetts, USA

    Archana Venkataraman

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

    Lauren O'Donnell

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

    Eleftheria Panagiotaki

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Reichenbach, A., Hlawitschka, M., Tittgemeyer, M., Scheuermann, G. (2014). Choosing a Tractography Algorithm: On the Effects of Measurement Noise. In: Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O'Donnell, L., Panagiotaki, E. (eds) Computational Diffusion MRI and Brain Connectivity. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-02475-2_11

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