A Kernel-Based Approach for User-Guided Fiber Bundling using Diffusion Tensor Data

Raul San Jose Estepar, Marek Kubicki, Martha Shenton, Carl-Fredrik Westin
2006 2006 International Conference of the IEEE Engineering in Medicine and Biology Society  
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
more » ... 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.
doi:10.1109/iembs.2006.259829 pmid:17946126 pmcid:PMC2768065 fatcat:nzg5wer6mjhxnjr5335hqnjfru