SMT: Split and Merge tractography for DT-MRI.
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.
Bozkaya
Uğur
U
Boğaziçi University, Electrical & Electronics Eng. Dept., VAVlab, Istanbul, Turkey.
Acar
Burak
B
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D009434:Neural Pathways / Q000166:cytology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity