Automated Identification of the Retinogeniculate Visual Pathway Using a High-Dimensional Tractography Atlas.

The retinogeniculate visual pathway (RGVP) plays an important role in the visual system. Diffusion MRI-based tractography has been successfully used to identify RGVP. However, challenges of RGVP tractography remain because of its highly curved path and intricate anatomical environment. One of the key challenges is the large false-positive fibers generated from RGVP tractography that requires the labor costs to hand-draw ROIs for fiber filtering. Therefore, we presented a pipeline to enable automated RGVP identification in dMRI tractography. First, we generated a tractography-based RGVP atlas. Herein, the multi-fiber unscented Kalman filter tractography was performed using high-resolution data from 50 subjects. Then, we transformed the 50 tractography cases into a common space and implemented data-driven fiber clustering to group the neighboring fibers with similar trajectories into one cluster. Two experienced anatomists were responsible for RGVP annotation in the tractography atlas. Second, the high-dimensional RGVP atlas was applied to identify subject-specific RGVP in testing datasets and two patients with different scanning parameters. Experimental results showed that our automatic identification results have ideal colocalization with expert manual identification in terms of hausdorff distance, fiber distance, and visualization. Therefore, the proposed method provides an efficient tool for analyzing large-scale datasets in vision-related neuroscience research.