Flow-based fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques.
In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly.
Campbell
Jennifer S W
JS
McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada. jcampbel@bic.mni.mcgill.ca
Siddiqi
Kaleem
K
Rymar
Vladimir V
VV
Sadikot
Abbas F
AF
Pike
G Bruce
GB
D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000033:anatomy & histology; D038524:Diffusion Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008348:Manikins; D015233:Models, Statistical; D009412:Nerve Fibers / Q000502:physiology; D015203:Reproducibility of Results