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Abstract #3469

A robust framework for characterising diffusion metrics of peripheral nerves: exploiting state of the art tracking methodsĀ 

Arkiev D'Souza1, Chenyu Wang1,2, Sicong Tu1,2, Dominic Soligo3, Matthew Kiernan1,2,4, Michael Barnett1,4, and Fernando Calamante1,5,6
1Brain and Mind Centre, The University of Sydney, Sydney, Australia, 2Central Clinical School, The University of Sydney, Sydney, Australia, 3I-MED Radiology Network, Camperdown, Australia, 4Department of Neurology, Royal Prince Alfred Hospital, Camperdown, Australia, 5School of Biomedical Engineering, The University of Sydney, Sydney, Australia, 6Sydney Imaging, The University of Sydney, Sydney, Australia

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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