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Optimized 3D brachial plexus MR neurography using deep learning reconstruction

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Skeletal Radiology Aims and scope Submit manuscript

Abstract

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-T2-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 (AC2) 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 (AC2: ‘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.

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Data Availability

The raw data used to support the findings of this study are available upon reasonable request from the corresponding author.

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Acknowledgements

The authors thank Shiv Kaushik and Maggie Fung for providing technical advice.

Funding

HSS receives institutional research support from GE Healthcare.

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Authors and Affiliations

  1. Department of Radiology and Imaging, Hospital for Special Surgery, 535 E. 70Th St., New York, NY, 10021, USA

    D. B. Sneag, S. C. Queler, G. Campbell, P. G. Colucci, J. Lin, Y. Lin, Q. Li & E. T. Tan

  2. Weill Medical College of Cornell, New York, NY, USA

    D. B. Sneag

  3. College of Medicine, SUNY Downstate Health Sciences University, Brooklyn, NY, USA

    S. C. Queler

  4. GE Healthcare, Waukesha, WI, USA

    Y. Wen

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  1. D. B. Sneag
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  5. J. Lin
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Correspondence to D. B. Sneag.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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D. B. Sneag, S. C. Queler joint first author.

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Sneag, D.B., Queler, S.C., Campbell, G. et al. Optimized 3D brachial plexus MR neurography using deep learning reconstruction. Skeletal Radiol 53, 779–789 (2024). https://doi.org/10.1007/s00256-023-04484-4

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  • DOI: https://doi.org/10.1007/s00256-023-04484-4

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