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

R. San Jose Estepar, Marek Kubicki, M. E. Shenton, C.-F. Westin
28th IEEE EMBS
Pages 2626-2629
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.

(a) Bundling of the Inferior Occipital Frontal Fasciculus (IOFF) and (b) the Uncinate Fasciculus (UF) using the proposed method. Computed bundles are highlighted in red.

Reference

San Jose Estepar R, Kubicki M, Shenton M, Westin CF. A kernel-based approach for user-guided fiber bundling using diffusion tensor data. In 28th IEEE EMBS. New York City, NY, USA, 2006;2626-2629.

Bibtex entry

@InProceedings{san-joseEMBS06,
  author         = {Raul {San Jose Estepar} and Marek Kubicki and Martha       
                   Shenton  and C.-F. Westin},                                 
  title          = {A Kernel-Based Approach for User-Guided Fiber Bundling     
                   using  Diffusion Tensor Data},                              
  booktitle      = {28th IEEE EMBS},                                           
  year           = {2006},                                                     
  pages          = {2626--2629},                                               
  address        = {New York City, NY, USA},                                   
  month          = {September}
}

Grants

NIH P41-RR13218 (NAC), NIH U24-RR021382

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