Manual vs. AI-Based Tractography: Assessing Fractional Anisotropy Consistency, Applicability and Methodological Implications

Tractography using diffusion tensor imaging (DTI) and constrained spherical deconvolution (CSD) provides valuable insights into the structure of white matter pathways. However, different methodologies may produce divergent fractional anisotropy (FA) values due to fundamental differences in their underlying approaches. This study compares FA measurements obtained using a manual DTI deterministic tractography method and an automatic AI-based approach via the TractSeg framework. Thirty healthy adults underwent brain MRI, and nine major white matter tracts were reconstructed using DTI-based vendor software and CSD with the AI-driven TractSeg.

FA measurements were analyzed using inter-rater reliability and agreement metrics, including intraclass correlation coefficients (ICCs). Results revealed substantial differences in FA between the two methods, with ICC values ranging from poor to moderate for most fibers. Normalization using FA values of the corpus callosum (CC) and comparison of relative values further highlighted impactful discrepancies for all of the fibers (p < 0.001). Manual DTI-based methods yielded higher FA values across most tracts, with the largest discrepancies observed in the CC and inferior fronto-occipital fasciculus. Conversely, AI-based TractSeg showed higher FA values for the uncinate fasciculus, demonstrating advantages for smaller, complex fibers.

Additionally, tract volume analysis showed that AI-based methods consistently produced larger tract volumes; however, volume differences did not align with FA ICC patterns. This indicates that volumetric discrepancies alone do not explain FA variability between methods. Despite high inter-rater reliability for manual measurements, significant inter-method differences indicate that FA values from the two methods are not interchangeable. Standardization is needed for reliable cross-study comparisons.