A Bayesian Approach for Stochastic White Matter Tractography


O. Friman, Gunnar Farneback, C.-F. Westin
TMI
Volume 25, Number 8, Pages 965-978
2006

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Abstract

White matter fiber bundles in the human brain can be located by tracing the local water diffusion in diffusion weighted magnetic resonance imaging (MRI) images. In this paper, a novel Bayesian modeling approach for white matter tractography is presented. The uncertainty associated with estimated white matter fiber paths is investigated, and a method for calculating the probability of a connection between two areas in the brain is introduced. The main merits of the presented methodology are its simple implementation and its ability to handle noise in a theoretically justified way. Theory for estimating global connectivity is also presented, as well as a theorem that facilitates the estimation of the parameters in a constrained tensor model of the local water diffusion profile.

(a) Fiber samples tracing the left and right Cingulum bundles. All the red fibers originate from the same seed point, as do all the yellow paths. The seed points are located where the bundles cross the axial slice. (b) 3000 fiber samples initiated in the splenium of Corpus callosum, see Fig. 9. The coloring indicates how the probability evolves along the fiber paths according to (5).


Reference

Friman O, Farneback G, Westin CF. A Bayesian approach for stochastic white matter tractography. TMI 2006;25(8):965-978.

Bibtex entry

@Article{frimanTMI06,
  author         = {Ola Friman and Gunnar Farneback and Carl-Fredrik Westin},  
  title          = {A {B}ayesian Approach for Stochastic White Matter          
                   Tractography},                                              
  journal        = {TMI},                                                      
  year           = {2006},                                                     
  volume         = {25},                                                       
  number         = {8},                                                        
  pages          = {965--978}
}                                                

Grants

NIH P41-RR13218 (NAC), NIH R01-MH50740, NIH U54-EB005149 (NAMIC)

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