A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements

Geoffrey J M Parker, Hamied A. Haroon, Claudia A M Wheeler-Kingshott

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Purpose: To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions. Materials and Methods: The commonly used streamline approach is adapted to exploit the uncertainty in the orientation-of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure. Results: Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen-start points or regions are generated in a number of major tracts. Conclusion: The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity. © 2003 Wiley-Liss, Inc.
    Original languageEnglish
    Pages (from-to)242-254
    Number of pages12
    JournalJournal of Magnetic Resonance Imaging
    Volume18
    Issue number2
    DOIs
    Publication statusPublished - 1 Aug 2003

    Keywords

    • Anatomic connectivity
    • Brain
    • Diffusion tensor imaging
    • Probability
    • Streamlines
    • Tractography

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