Abstract #3469

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

Arkiev D'Souza¹, Chenyu Wang^1,2, Sicong Tu^1,2, Dominic Soligo³, Matthew Kiernan^1,2,4, Michael Barnett^1,4, and Fernando Calamante^1,5,6

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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