A robust framework for characterising diffusion metrics of the median and ulnar nerves: Exploiting state-of-the-art tracking methods

Arkiev D'Souza; Chenyu Wang; Sicong Tu; Domenic J Soligo; Matthew C Kiernan; Michael Barnett; Fernando Calamante

doi:10.1111/jns.12478

A robust framework for characterising diffusion metrics of the median and ulnar nerves: Exploiting state-of-the-art tracking methods

J Peripher Nerv Syst. 2022 Mar;27(1):67-83. doi: 10.1111/jns.12478. Epub 2021 Dec 27.

Authors

Arkiev D'Souza^{1

2}, Chenyu Wang^{1

3}, Sicong Tu^{1

4}, Domenic J Soligo⁵, Matthew C Kiernan^{1

4

6}, Michael Barnett^{1

3

6}, Fernando Calamante^{1

2

7}

Affiliations

¹ Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia.
² Sydney Imaging, The University of Sydney, Sydney, New South Wales, Australia.
³ Sydney Neuroimaging Analysis Centre, Camperdown, New South Wales, Australia.
⁴ Central Clinical School, The University of Sydney, Sydney, New South Wales, Australia.
⁵ I-MED Radiology Network, Camperdown, New South Wales, Australia.
⁶ Department of Neurology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia.
⁷ School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, Australia.

PMID: 34908209
DOI: 10.1111/jns.12478

Abstract

Diffusion-weighted imaging has been used to quantify peripheral nerve properties; however, traditional post-processing techniques have several limitations. Advanced neuroimaging techniques, which overcome many of these limitations, have not been applied to peripheral nerves. Here, we use state-of-the-art diffusion analysis tools to reconstruct the median and ulnar nerves and quantify their diffusion properties. Diffusion-weighted MRI scans were obtained from eight healthy adult subjects. Constrained spherical deconvolution was combined with probabilistic fibre tracking to compute track-weighted fibre orientation distribution (TW-FOD). The tensor was computed and used along with the tracks to estimate TW apparent diffusion coefficient (TW-ADC), TW fractional anisotropy (TW-FA), TW axial diffusivity (TW-AD), and TW radial diffusivity (TW-RD). Variability of TW measurements was used to estimate power size information. The population intersession mean (± SD) measurements for the median nerve were TW-FOD 1.30 (±0.17), TW-ADC 1.16 (±0.13) × 10^-3 mm² /s, TW-FA 0.60 (±0.05), TW-AD 2.05 (±0.16) × 10^-3 mm² /s, and TW-RD 0.72 (±0.12) × 10^-3 mm² /s. The corresponding measurements for the ulnar nerve were TW-FOD 1.25 (±0.14), TW-ADC 1.13 (±0.10) × 10^-3 mm² /s, TW-FA 0.56 (±0.06), TW-AD 1.93 (±0.01) × 10^-3 mm² /s, and TW-RD 0.74 (±0.12) × 10^-3 mm² /s. Based on these measurements, a sample size of 37 would be sufficient to detect a 10% difference in any of the measured TW metrics. A sample size of 20 would be large enough to detect within-subject differences as small as 2.9% (TW-AD, ulnar nerve) and between-subject differences as small as 3.8% (TW-AD, ulnar nerve).

Keywords: diffusion magnetic resonance imaging; median nerve; peripheral nerve imaging; track-weighted imaging; ulnar nerve.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Anisotropy
Benchmarking
Diffusion Tensor Imaging* / methods
Humans
Median Nerve / diagnostic imaging
Ulnar Nerve* / diagnostic imaging