White matter fiber tract segmentation methods provide crucial neural pathway reference information for brain connectivity analysis by identifying white matter tracts connecting distinct brain regions. Traditional segmentation methods predominantly depend on diffusion magnetic resonance imaging （dMRI）， but the lengthy acquisition time of dMRI severely restricts its clinical applicability. To address this limitation， this paper introduces a white matter fiber tract segmentation approach based on T1-weighted imaging. This method leverages the structural tensor of T1-weighted images to infer potential fiber orientations， thereby enhancing the segmentation accuracy of white matter tracts. Moreover， the proposed method incorporates privileged information from dMRI during model training to guide the learning process， thus improving the performance of the white matter tract segmentation model， and the segmentation of challenging tracts is improved significantly， with a 5% improvement in Dice score for the left fornix （FX_left） and a 6% improvement in Dice score for the right fornix （FX_right）. This approach mitigates the limitations of conducting neural pathway analysis in the absence of dMRI， broadening the application scope of neural pathway analysis.