BootGraph: Probabilistic Fiber Tracking Using Bootstrap Algorithm and Graph Theory

Robert Stefan Vorburger¹, Carolin Reischauer¹, Peter Boesiger¹

Bootstrap algorithms and graph theory are sophisticated methods in diffusion tensor imaging to obtain probabilistic connectivity maps in the human brain. In the present work the two methods are combined by weighting the graph edges with the statistics derived from the bootstrap approach. Hence, the resulting connectivity maps reflect not only directional probabilities but also the uncertainty in the measured data. Thereby, the time consuming bootstrap calculations have to be performed only once and can be used for different settings of tracking parameters, such as the FA threshold or curvature restriction.