Improvement of White Matter Fiber Tracking Based on Diffusion-Tensor MR Imaging Data Using Modified Speed Functions

Background/objective: white matter tractography is a non-invasive method, which reconstructs three-dimensional trajectories of the brain tracts using diffusion-tensor imaging (dti) data. due to the partial volume effect of dti data, some of tractography algorithms are unable to follow the correct pathways after the crossing and branching regions. the main challenge for tractography methods has been the ability to detect these regions. fast marching techniques are capable of tracking the fibers with wide spreading. materials and methods: in order to detect true fibers, an adaptive functional anisotropy (fa) weighted function is proposed to modify the speed function of these algorithms. the performance of the proposed tractography method is assessed using synthetic data and its feasibility is showed by extracting some well-known tracts using healthy human dti datasets. result: the percentage of the length of whole tracts extracted by our proposed method is above 85% even for a signal to noise ratio (snr) level equal to 16. the ability of this method to detect the fiber crossing in simulation data is above 90%. furthermore, the tractography results of some well-known tracts demonstrate the ability of the proposed methods to extract the correct pathways from the anatomical point of view. conclusion: this method has led to great impact on the fast-marching fiber-tracking method in propagating the tractography front in an adaptive manner. the suggested speed function can make the speed of front propagation adapted to the type of brain’s environments such as isotropic and anisotropic regions.