Abstract #3959

A New Comprehensive Framework for Probabilistic Tractography of Fanning Fibres

Jennifer Campbell¹, Parya MamayyezSiahkal², Peter Savadjiev³, Ilana R. Leppert¹, Kaleem Siddiqi², G. B. Pike¹

¹McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; ²Centre for Intelligent Machines, McGill University; ³Brigham & Women's Hospital, Harvard University

The objective of the current work was to develop an improved probabilistic tractography framework that could handle, in addition to crossing fibres, information on more complex subvoxel geometries, such as fanning fibres. The technique incorporates a residual bootstrap probabilistic processing step, followed by a tractography process that results in the assignment of an index of connectivity, at each voxel in the volume, to the region of interest of the user's choice. This connectivity index is derived using a weakest link approach, and solves many of the problems inherent in popular connectivity indices that are based on frequency of connection.