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

R. San Jose Estepar, M. Kubicki, M. E. Shenton, C. F. Westin
Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS
Pages 2626-9
September, 2006

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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.

Reference

Estepar RSJ, Kubicki M, Shenton ME, Westin CF. A kernel-based approach for user-guided fiber bundling using diffusion tensor data. In Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS. 2006;2626-9.

Grants

NIH P41 RR13218, U24 RR021382

Research area

dti
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