Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2021-11, Vol.242, p.118451-118451, Article 118451
• Main Authors: Schilling, Kurt G., Tax, Chantal M.W., Rheault, Francois, Hansen, Colin, Yang, Qi, Yeh, Fang-Cheng, Cai, Leon, Anderson, Adam W., Landman, Bennett A.
• Format: Article
• Language: English
• Subjects: Anisotropy; Bundle segmentation; Datasets; Diffusion; Diffusion Tensor Imaging - methods; Harmonization; Humans; Image Processing, Computer-Assisted; Investigations; Neural networks; Reproducibility; Reproducibility of Results; Scanners; Segmentation; Substantia alba; Substantia grisea; Tractography; Variation; White matter; White Matter - diagnostic imaging
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118451

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes