SMT: split and merge tractography for DT-MRI

  • Authors:

  • Uğur Bozkaya;Burak Acar

  • Affiliations:

  • Boğaziçi University, Electrical & Electronics Eng. Dept., VAVlab, İİstanbul, Turkey;Boğaziçi University, Electrical & Electronics Eng. Dept., VAVlab, İİstanbul, Turkey

  • Venue:
  • MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
  • Year:
  • 2007

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Abstract

Diffusion tensor magnetic resonance imaging (DT-MRI) based fiber tractography aims at reconstruction of the fiber network of brain. Most commonly employed techniques for fiber tractography are based on the numerical integration of the principal diffusion directions. Although these approaches generate intuitive and easy to interpret results, they are prone to cumulative errors and mostly discard the stochastic nature of DT-MRI data. The proposed Split & Merge Tractography (SMT) technique aims at overcoming the drawbacks of fiber tractography by incorporating it with Markov Chain Monte Carlo techniques. SMT is based on clustering diversely distributed short fiber tracts based on their inter-connectivity. SMT also provides real-time interaction to adjust a user defined confidence level for clustering.