An improved fiber tracking algorithm based on fiber assignment using the continuous tracking algorithm and two-tensor model

This study tested an improved fiber tracking algorithm, which was based on fiber assignment using a continuous tracking algorithm and a two-tensor model. Different models and tracking decisions were used by judging the type of estimation of each voxel. Thismethod should solve the cross-track problem. This study included eight healthy subjects, two axonal injury patients and seven demyelinating disease patients. This new algorithm clearly exhibited a difference in nerve fiber direction between axonal injury and demyelinating disease patients and healthy control subjects. Compared with fiber assignment with a continuous tracking algorithm, our novel method can track more and longer nerve fibers, and also can solve the fiber crossing problem.

Diffusion tensor imaging fiber tracking with reliable tracking orientation and flexible step size

We propose a method of reliable tracking orientation and flexible step size fiber tracking. A new directional strategy was defined to select one optimal tracking orientation from each directional set, which was based on the single-tensor model and the two-tensor model. The directional set of planar voxels contained three tracking directions： two from the two-tensor model and one from the single- tensor model. The directional set of linear voxels contained only one principal vector. In addition, a flexible step size, rather than fixable step sizes, was implemented to improve the accuracy of fiber tracking. We used two sets of human data to assess the performance of our method; one was from a healthy volunteer and the other from a patient with low-grade glioma. Results verified that our method was superior to the single-tensor Fiber Assignment by Continuous Tracking and the two-tensor eXtended Streamline Tractography for showing detailed images of fiber bundles.

Tri-linear interpolation-based cerebral white matter fiber imaging

Diffusion tensor imaging is a unique method to visualize white matter fibers three-dimensionally, non-invasively and in vivo, and therefore it is an important tool for observing and researching neural regeneration. Different diffusion tensor imaging-based fiber tracking methods have been already investigated, but making the computing faster, fiber tracking longer and smoother and the details shown clearer are needed to be improved for clinical applications. This study proposed a new fiber tracking strategy based on tri-linear interpolation. We selected a patient with acute infarction of the right basal ganglia and designed experiments based on either the tri-linear interpolation algorithm or tensorline algorithm. Fiber tracking in the same regions of interest （genu of the corpus callosum） was performed separately. The validity of the tri-linear interpolation algorithm was verified by quan- titative analysis, and its feasibility in clinical diagnosis was confirmed by the contrast between tracking results and the disease condition of the patient as well as the actual brain anatomy. Statis- tical results showed that the maximum length and average length of the white matter fibers tracked by the tri-linear interpolation algorithm were significantly longer. The tracking images of the fibers indicated that this method can obtain smoother tracked fibers, more obvious orientation and clearer details. Tracking fiber abnormalities are in good agreement with the actual condition of patients, and tracking displayed fibers that passed though the corpus callosum, which was consistent with the anatomical structures of the brain. Therefore, the tri-linear interpolation algorithm can achieve a clear, anatomically correct and reliable tracking result.

Longitudinal diffusion tensor magnetic resonance imaging analysis at the cohort level reveals disturbed cortical and callosal microstructure with spared corticospinal tract in the TDP-43^(G298S) ALS mouse model

Background:In vivo diffusion tensor imaging(DTI)of the mouse brain was used to identify TDP-43 associated alterations in a mouse model for amyotrophic lateral sclerosis(ALS).Methods:Ten mice with TDP-43^(G298S) overexpression under control of the Thy1.2 promoter and 10 wild type(wt)underwent longitudinal DTI scans at 11.7 T,including one baseline and one follow-up scan with an interval of about 5months.Whole brain-based spatial statistics(WBSS)of DTI-based parameter maps was used to identify longitudinal alterations of TDP-43^(G298S) mice compared to wt at the cohort level.Results were supplemented by tractwise fractional anisotropy statistics(TFAS)and histological evaluation of motor cortex for signs of neuronal loss.Results:Alterations at the cohort level in TDP-43^(G298S) mice were observed cross-sectionally and longitudinally in motor areas M1/M2 and in transcallosal fibers but not in the corticospinal tract.Neuronal loss in layer V of motor cortex was detected in TDP-43^(G298S) at the later(but not at the earlier)timepoint compared to wt.Conclusion:DTI mapping of TDP-43^(G298S) mice demonstrated progression in motor areas M1/M2.WBSS and TFAS are useful techniques to localize TDP-43^(G298S) associated alterations over time in this ALS mouse model,as a biological marker.