Longitudinal tractography with application to neuronal fiber trajectory reconstruction in neonates

Pew Thian Yap, John H. Gilmore, Weili Lin, Dinggang Shen

Research output: Contribution to journalConference article

8 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)66-73
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
Publication statusPublished - 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 2011 Sep 182011 Sep 22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Longitudinal tractography with application to neuronal fiber trajectory reconstruction in neonates'. Together they form a unique fingerprint.

  • Cite this