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Longitudinal Tractography with Application to Neuronal Fiber Trajectory Reconstruction in Neonates

  • Pew-Thian Yap
  • John H. Gilmore
  • Weili Lin
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

This paper presents a novel tractography algorithm for more accurate reconstruction of fiber trajectories in low SNR diffusion-weighted images, such as neonatal scans. We leverage information from a later-time-point longitudinal scan to obtain more reliable estimates of local fiber orientations. Specifically, we determine the orientation posterior probability at each voxel location by utilizing prior information given by the longitudinal scan, and with the likelihood function formulated based on the Watson distribution. We incorporate this Bayesian model of local orientations into a state-space model for particle-filtering-based probabilistic tracking, catering for the possibility of crossing fibers by modeling multiple orientations per voxel. Regularity of fibers is enforced by encouraging smooth transitions of orientations in subsequent locations traversed by the fiber. Experimental results performed on neonatal scans indicate that fiber reconstruction is significantly improved with less stray fibers and is closer to what one would expect anatomically.

Keywords

Prior Information Connectivity Matrix Connectivity Matrice Probabilistic Tracking Longitudinal Scan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pew-Thian Yap
    • 1
  • John H. Gilmore
    • 2
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
  1. 1.BRIC, Department of RadiologyUniversity of North CarolinaChapel HillUSA
  2. 2.Department of PyschiatryUniversity of North CarolinaChapel HillUSA

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