Quantitative Anisotropy (QA) improves deterministic diffusion fiber tracking by better identifying fiber orientations and reducing false tracks. The study compared QA with fractional anisotropy (FA) and generalized fractional anisotropy (GFA) in phantom and in vivo experiments. Phantom studies showed that QA is less sensitive to partial volume effects from crossing fibers and free water, making it a robust index for tracking. QA-aided tractography had better spatial resolution than FA- and GFA-aided methods. In vivo studies tracking the arcuate fasciculus showed that QA-aided tractography had significantly fewer false tracks than FA-, GFA-, and anatomy-aided methods. In the shell scheme (HARDI), QA-aided tractography had 16.2% false tracks, while FA-aided, GFA-aided, and anatomy-aided methods had 30.7%, 32.6%, and 24.45% false tracks, respectively. In the grid scheme (DSI), QA-aided tractography had 4.43% false tracks, while FA-aided, GFA-aided, and anatomy-aided methods had 12.3%, 9.0%, and 10.93% false tracks, respectively. QA-aided tractography outperformed other methods in both schemes. QA filtering effectively removed noisy fibers and defined accurate termination locations. The QA-aided tractography also showed good agreement with white matter regions segmented from T1-weighted images. The study also demonstrated that QA-based deterministic fiber tracking can be used as a general algorithm that can incorporate any voxel-based or ODF-based index. The algorithm was implemented in DSI Studio and showed comparable performance to constrained spherical deconvolution (CSD) tractography. The study concluded that QA improves deterministic fiber tracking by better identifying fiber orientations and reducing false tracks, making it a valuable tool for human connectomics.Quantitative Anisotropy (QA) improves deterministic diffusion fiber tracking by better identifying fiber orientations and reducing false tracks. The study compared QA with fractional anisotropy (FA) and generalized fractional anisotropy (GFA) in phantom and in vivo experiments. Phantom studies showed that QA is less sensitive to partial volume effects from crossing fibers and free water, making it a robust index for tracking. QA-aided tractography had better spatial resolution than FA- and GFA-aided methods. In vivo studies tracking the arcuate fasciculus showed that QA-aided tractography had significantly fewer false tracks than FA-, GFA-, and anatomy-aided methods. In the shell scheme (HARDI), QA-aided tractography had 16.2% false tracks, while FA-aided, GFA-aided, and anatomy-aided methods had 30.7%, 32.6%, and 24.45% false tracks, respectively. In the grid scheme (DSI), QA-aided tractography had 4.43% false tracks, while FA-aided, GFA-aided, and anatomy-aided methods had 12.3%, 9.0%, and 10.93% false tracks, respectively. QA-aided tractography outperformed other methods in both schemes. QA filtering effectively removed noisy fibers and defined accurate termination locations. The QA-aided tractography also showed good agreement with white matter regions segmented from T1-weighted images. The study also demonstrated that QA-based deterministic fiber tracking can be used as a general algorithm that can incorporate any voxel-based or ODF-based index. The algorithm was implemented in DSI Studio and showed comparable performance to constrained spherical deconvolution (CSD) tractography. The study concluded that QA improves deterministic fiber tracking by better identifying fiber orientations and reducing false tracks, making it a valuable tool for human connectomics.