The quality of fiber bundles for tractography is linked to dMRI acquisition parameters, with higher quality from costly multi-shell exams. This study examines whether the new FOD-Swin-Net (FSN) angular super-resolution deep learning method matches the quality and technique of multishell multi-tissue (MSMT-CSD) reconstruction, while reducing computational demands and acquisition complexity with an initial single-shell acquisition. We also compare FSN to the single-shell derived FOD standard (SS3T-CSD). Fascicle segmentation in deep and superficial white matter was performed using atlases. By calculating bundle masks and density maps we computed similarity metrics including streamline-based bundle adjacency, voxel-based bundle adjacency, voxel-based density correlation, and weighted Dice coefficient. The findings reveal that approximately 82.6% of the evaluated fascicles show a greater similarity between FSN and the MSMT-CSD technique, and of this percentage, 56.7% exhibit a significant difference, indicating potential cost-effective benefits.