Erom Lucas Alves Freitas

Background: This study aimed to compare diffusion tensor imaging (DTI) and constrained spherical deconvolution (CSD) techniques in the segmentation of the dentate-rubro-thalamic tract (DRTT), evaluating their anatomical accuracy and applicability for surgical planning in deep brain stimulation (DBS) for essential tremor surgery in two patients. Methods: The images were acquired using two 1.5 Tesla magnetic resonance imaging protocols optimized for both DTI and CSD. Preprocessing included noise removal, artifact correction, and distortion adjustments. Segmentation was performed using region-of-interest definitions from specific atlases. CSD was applied with response function estimation, followed by fiber orientation reconstruction and tracking using the probabilistic improved fiber orientation distribution 2(iFOD2) algorithm. The DTI technique was conducted with tensor calculation and fractional anisotropy (FA) analysis. Volume and FA metrics were compared between techniques to evaluate segmentation accuracy for the DRTT. Results: CSD-based segmentation showed significantly larger volumes in the left hemisphere compared to DTI, along with higher FA values. In the right hemisphere, the difference was not statistically significant. Dice similarity index analysis revealed very low correspondence between the techniques in both hemispheres, suggesting greater precision of CSD in DRTT segmentation. Conclusion: CSD proved to be more effective in DRTT segmentation, with better angular resolution and greater detailing of axonal trajectories, especially in regions with fiber crossing.

Background: Tractography of cerebral white matter tracts is a technique with applications in neurosurgical planning and the diagnosis of neurological diseases. In this context, the approach based on the constrained spherical deconvolution (CSD) algorithm allows for more efficient and plausible segmentations. This study aimed to compare two CSD techniques for corticospinal tract (CST) segmentation. Methods: This study examined 40 diffusion-weighted images (DWIs) acquired at 7T from healthy participants in the human connectome project (HCP) and 12 clinical 1.5T DWIs from patients undergoing neurosurgical procedures. Tractography was performed using two techniques: regions of interest-based approach and an automatic approach using the TractSeg neural network. The volume of the CST segmented by the two methods was compared using the Dice similarity coefficient. Results: There was a low similarity between the CST volumes segmented by the two techniques (Dice index for the HCP: 0.479 ± 0.04; Dice index for the Clinical: 0.404 ± 0.08). However, both techniques achieved high levels of consistency in sequential measurements, with intraclass correlation coefficient values above 0.995 for all comparisons. In addition, all selected metrics showed significant differences when comparing the two techniques (HCP – volume P < 0.0001, fractional anisotropy [FA] P = 0.0061, mean diffusivity [MD] P < 0.0001; Clinical – volume P < 0.0001, FA P = 0.0018, MD P = 0.0018). Conclusion: Both methods demonstrate a high degree of consistency; however, the automatic approach appears to be more consistent overall. When comparing the CST segmentations between the two methods, we observed only a moderate similarity and differences in all considered metrics.