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Repeated Tractography of a Single Subject: How High Is the Variance?

Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.

Notes

Acknowledgements

We thank Russell Poldrack and his colleagues for sharing the data from the MyConnectome project. We also thank Cyril Pernet and his colleagues for sharing neuroimaging data from brain tumor patients. The Nvidia Corporation is acknowledged for the donation of the Tesla K40 graphics card. This research was supported by the Information Technology for European Advancement (ITEA) 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy) and the Swedish Research Council (grant 2015-05356, “Learning of sets of diffusion MRI sequences for optimal imaging of micro structures”). Anders Eklund was also supported by Swedish Research Council Grant 2013-5229 (“Statistical Analysis of fMRI Data”).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Division of Medical Informatics, Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
  2. 2.Center for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden
  3. 3.Division of Statistics and Machine Learning, Department of Computer and Information ScienceLinköping UniversityLinköpingSweden

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