Abstract
Tractography algorithms have been developed to reconstruct likely WM pathways in the brain from diffusion tensor imaging (DTI) data. In this study, an elegant and simple means for improving existing tractography algorithms is proposed by allowing tracts to propagate through diagonal trajectories between voxels, instead of only rectilinearly to their facewise neighbors. A series of tests (using both real and simulated data sets) are utilized to show several benefits of this new approach. First, the inclusion of diagonal tract propagation decreases the dependence of an algorithm on the arbitrary orientation of coordinate axes and therefore reduces numerical errors associated with that bias (which are also demonstrated here). Moreover, both quantitatively and qualitatively, including diagonals decreases overall noise sensitivity of results and leads to significantly greater efficiency in scanning protocols; that is, the obtained tracts converge much more quickly (i.e., in a smaller amount of scanning time) to those of data sets with high SNR and spatial resolution. Importantly, the inclusion of diagonal propagation adds essentially no appreciable time of calculation or computational costs to standard methods. This study focuses on the widely-used streamline tracking method, FACT (fiber assessment by continuous tracking), and the modified method is termed “FACTID” (FACT including diagonals).
Highlights
Diffusion tensor imaging (DTI) is a useful MR technique for non-invasively investigating structural properties of neural white matter (WM) by measuring the random and constantly occurring motion of fluid and aqueous tissue particles
We propose an elegant and simple means for improving existing tractography algorithms and for decreasing numerical artifacts by allowing tracts to propagate through diagonal trajectories between voxels instead of only rectilinearly to their facewise neighbors
We focus on the widely-used FACT method of tract propagation for streamline tracking which is utilized, for example, in the widely used dtiStudio analysis package [22]
Summary
Diffusion tensor imaging (DTI) is a useful MR technique for non-invasively investigating structural properties of neural white matter (WM) by measuring the random and constantly occurring motion of fluid and aqueous tissue particles. DTI tractography has been shown to provide a means for estimating the topology of WM tracts in vivo, increasing our understanding of brain physiology, pathology and structural connectivity in both clinical and research-oriented applications, e.g., [1,2,3,4,5,6,7,8]. This technique has been shown to successfully reproduce many known WM pathways and to be useful in planning for neurosurgical operations. Non tensor-based models of diffusion have been developed, such as high-angular resolution (HARDI) techniques [21]
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