The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. Here we show that global probabilistic tractography with anatomical priors can be trained on such data, which can only be acquired on a handful of Connectome scanners worldwide, and improve the accuracy of tractography in more widely available, routine-quality diffusion data. We apply this method to reconstruct the three subcomponents of the SLF and show its superior accuracy compared to a conventional multi-ROI approach.
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