Diffusion MRI has previously been used to quantify peripheral nerves; however, traditional post-processing techniques have several limitations. Here, we demonstrate the feasibility of using state-of-the-art diffusion analysis tools to reconstruct and quantify the ulnar nerve of the forearm. Constrained-spherical deconvolution was combined with probabilistic fibre-tracking to compute several track-weighted measurements in the ulnar nerve. The results suggest that a sample size of 22 would be sufficient to detect a 10% difference in any of the measured track-weighted metrics, and a sample size of 20 would be large enough to detect within-subject differences as small as 3%.
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