A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements.
OBJECTIVE
To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions.


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.


CONCLUSIONS
The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity.
Parker
Geoffrey J M
GJ
Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK. geoff.parker@man.ac.uk
Haroon
Hamied A
HA
Wheeler-Kingshott
Claudia A M
CA
D016880:Anisotropy; D001921:Brain / Q000033:anatomy & histology*; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D017352:Echo-Planar Imaging; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D035501:Uncertainty