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A kernel-based approach for user-guided fiber bundling using diffusion tensor data

Citation:

San José Estépar R, Kubicki M, Shenton M, Westin C-F. A kernel-based approach for user-guided fiber bundling using diffusion tensor data. Conf Proc IEEE Eng Med Biol Soc 2006;1:2626-9.

Date Published:

2006

Abstract:

This paper describes a novel user-guided method for grouping fibers from diffusion tensor MRI tractography into bundles. The method finds fibers, that passing through user-defined ROIs, still fit to the underlying data model given by the diffusion tensor. This is achieved by filtering the data and the ROIs with a kernel derived from a geodesic metric between tensors. A standard approach using binary decisions defining tracts passing through ROIs is critically dependent on ROIs that includes all trace lines of interest. The method described in this paper uses a softer decision mechanism through a kernel which enables grouping of bundles driven less exact, or even single point, ROIs. The method analyzes the responses obtained from the convolution with a kernel function along the fiber with the ROI data. Results in real data shows the feasibility of the approach to fiber bundling.