Abstract #1426

Tractography based white matter tract segmentation is robust to anisotropic resolutions

Elyssa Margaret McMaster¹, Nancy Newlin², Chloe Cho³, Jongyeon Yoon², Francois Rheault⁴, Bennett Landman^1,2,3,5,6, and Kurt Schilling^5,6

¹Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States, ²Department of Computer Science, Vanderbilt University, Nashville, TN, United States, ³Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ⁴Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada, ⁵Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ⁶Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States

Synopsis

Keywords: Tractography, Tractography

Motivation: Several studies have established tractography’s sensitivity to anisotropic spatial sampling; however, the direct comparison between the performance of different algorithms has only been shown quantitatively on a simulated dataset.

Goal(s): We aim to characterize the effect of anisotropic spatial sampling on bundle-wise tractography across algorithms compared to a high-resolution ground truth.

Approach: We down-sample high-resolution ground truth data to anisotropic sampling. We up-sample the anisotropic data to isotropic for further comparison.

Results: We show that tractography is robust to anisotropy with minimal biases if the beginning and end of pathways are well-defined.

Impact: We show that some algorithms, when paired with well-defined beginning and endpoints for major pathways, show stability in highly anisotropic data.

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