Estepar, Raul San JoseKubicki, MarekShenton, MarthaWestin, Carl-Fredrik2017-08-162006San José Estépar R, Kubicki M, Shenton M, Westin CF. 2006. A kernel-based approach for user-guided fiber bundling using diffusion tensor data. Conf Proc IEEE Eng Med Biol Soc 1: 2626-9. PMID: 17946126; PMCID: PMC2768065. doi:10.1109/IEMBS.2006.259829http://nrs.harvard.edu/urn-3:HUL.InstRepos:33766567This 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.en-USJournal Article2017-08-1610.1109/IEMBS.2006.259829