Analyzing and Reducing DTI Tracking Uncertainty by Combining Deterministic and Stochastic Approaches

Abstract

Diffusion Tensor Imaging (DTI) in combination with fiber tracking algorithms enables visualization and characterization of white matter structures in the brain. However, the low spatial resolution associated with the inherently low signal-to-noise ratio of DTI has raised concerns regarding the reliability of the obtained fiber bundles. Therefore, recent advancements in fiber tracking algorithms address the accuracy of the reconstructed fibers. In this paper, we propose a novel approach for analyzing and reducing the uncertainty of densely sampled 3D DTI fibers in biological specimens. To achieve this goal, we derive the uncertainty in the reconstructed fiber tracts using different deterministic and stochastic fiber tracking algorithms. Through a unified representation of the derived uncertainty, we generate a new set of reconstructed fiber tracts that has a lower level of uncertainty. We will discuss our approach in detail and present the results we could achieve when applying it to several use cases.