Longitudinal Tractography with Application to Neuronal Fiber Trajectory Reconstruction in Neonates

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.