Knowledge-based automated reconstruction of human brain white matter tracts using a path-finding approach with dynamic programming

Muwei Li, J. Tilak Ratnanather, Michael I. Miller, Susumu Mori

Research output: Contribution to journalArticlepeer-review

Abstract

It has been shown that the anatomy of major white matter tracts can be delineated using diffusion tensor imaging (DTI) data. Tract reconstruction, however, often suffers from a large number of false-negative results when a simple line propagation algorithm is used. This limits the application of this technique to only the core of prominent white matter tracts. By employing probabilistic path-generation algorithms, connectivity between a larger number of anatomical regions can be studied, but an increase in the number of false-positive results is inevitable. One of the causes of the inaccuracy is the complex axonal anatomy within a voxel; however, high-angular resolution (HAR) methods have been proposed to ameliorate this limitation. However, HAR data are relatively rare due to the long scan times required and the low signal-to-noise ratio. In this study, we tested a probabilistic path-finding method in which two anatomical regions with known connectivity were pre-defined and a path that maximized agreement with the DTI data was searched. To increase the accuracy of the trajectories, knowledge-based anatomical constraints were applied. The reconstruction protocols were tested using DTI data from 19 normal subjects to examine test-retest reproducibility and cross-subject variability. Fifty-two tracts were found to be reliably reconstructed using this approach, which can be viewed on our website.

Original languageEnglish (US)
Pages (from-to)271-281
Number of pages11
JournalNeuroImage
Volume88
DOIs
StatePublished - Mar 2014

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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