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.

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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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