Original Research

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

  1. Geoffrey J.M. Parker PhD1,*,
  2. Hamied A. Haroon MSc1,
  3. Claudia A.M. Wheeler-Kingshott PhD2

Article first published online: 21 JUL 2003

DOI: 10.1002/jmri.10350

Issue

Journal of Magnetic Resonance Imaging

Journal of Magnetic Resonance Imaging

Volume 18, Issue 2, pages 242–254, August 2003

Additional Information

How to Cite

Parker, G. J.M., Haroon, H. A. and Wheeler-Kingshott, C. A.M. (2003), A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J. Magn. Reson. Imaging, 18: 242–254. doi: 10.1002/jmri.10350

Author Information

  1. 1

    Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK

  2. 2

    NMR Research Unit, Institute of Neurology, University College London, London, UK

*Imaging Science and Biomedical Engineering, Stopford Building, University of Manchester, Oxford Road, Manchester M13 9PT, UK

  1. †

    This work appeared in early form at the ISMRM Workshop on Diffusion MRI (Biophysical Issues), Saint-Malo, France, 2002, and at the ISMRM Annual Meeting, Honolulu, 2002.

Publication History

  1. Issue published online: 21 JUL 2003
  2. Article first published online: 21 JUL 2003
  3. Manuscript Accepted: 21 APR 2003
  4. Manuscript Received: 25 NOV 2002

Funded by

  • MS Society of Great Britain and Northern Ireland

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Keywords:

  • diffusion tensor imaging;
  • brain;
  • tractography;
  • streamlines;
  • probability;
  • anatomic connectivity

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. J. Magn. Reson. Imaging 2003;18:242–254. © 2003 Wiley-Liss, Inc.

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