Optimized 3D brachial plexus MR neurography using deep learning reconstruction

Objective To evaluate whether ‘fast,’ unilateral, brachial plexus, 3D magnetic resonance neurography (MRN) acquisitions with deep learning reconstruction (DLR) provide similar image quality to longer, ‘standard’ scans without DLR. Materials and methods An IRB-approved prospective cohort of 30 subjects (13F; mean age = 50.3 ± 17.8y) underwent clinical brachial plexus 3.0 T MRN with 3D oblique-coronal STIR-T 2- weighted-FSE. ‘Standard’ and ‘fast’ scans (time reduction = 23–48%, mean = 33%) were reconstructed without and with DLR. Evaluation of signal-to-noise ratio (SNR) and edge sharpness was performed for 4 image stacks: ‘standard non-DLR,’ ‘standard DLR,’ ‘fast non-DLR,’ and ‘fast DLR.’ Three raters qualitatively evaluated ‘standard non-DLR’ and ‘fast DLR’ for i) bulk motion (4-point scale), ii) nerve conspicuity of proximal and distal suprascapular and axillary nerves (5-point scale), and iii) nerve signal intensity, size, architecture, and presence of a mass (binary). ANOVA or Wilcoxon signed rank test compared differences. Gwet’s agreement coefficient (AC 2 ) assessed inter-rater agreement. Results Quantitative SNR and edge sharpness were superior for DLR versus non-DLR (SNR by + 4.57 to + 6.56 [ p < 0.001] for ‘standard’ and + 4.26 to + 4.37 [ p < 0.001] for ‘fast;’ sharpness by + 0.23 to + 0.52/pixel for ‘standard’ [ p < 0.018] and + 0.21 to + 0.25/pixel for ‘fast’ [ p < 0.003]) and similar between ‘standard non-DLR’ and ‘fast DLR’ (SNR: p = 0.436–1, sharpness: p = 0.067–1). Qualitatively, ‘standard non-DLR’ and ‘fast DLR’ had similar motion artifact, as well as nerve conspicuity, signal intensity, size and morphology, with high inter-rater agreement (AC 2 : ‘standard’ = 0.70–0.98, ‘fast DLR’ = 0.69–0.97). Conclusion DLR applied to faster, 3D MRN acquisitions provides similar image quality to standard scans. A faster, DL-enabled protocol may replace currently optimized non-DL protocols.