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Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction

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Skeletal Radiology Aims and scope Submit manuscript

Abstract

Objective

To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning–based reconstruction (DLR) methods for evaluation of shoulder.

Materials and methods

We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics.

Results

Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600–0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691–1).

Conclusion

Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.

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Data availability

The data that support the findings of this study are available on request from the corresponding author, Jisook Yi. The data are not publicly available due to (restrictions, e.g., they are containing information that could compromise the privacy of research participants).

Abbreviations

CR:

Conventional reconstruction

DLR:

Deep-learning based reconstruction

SSC:

Subscapularis tendon

SST-IST:

Supraspinatus-infraspinatus tendon

GL:

Glenoid labrum

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Acknowledgements

The authors specially thanks to the statistical support from Hyungwoong Kim (Clinical trial center, Inje University Haeundae Paik Hospital).

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Authors and Affiliations

  1. Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea, Republic of Korea

    Seok Hahn, Jisook Yi, Ho-Joon Lee & Yedaun Lee

  2. GE Healthcare, Seoul, Korea

    Joonsung Lee

  3. GE Healthcare, Houston, TX, USA

    Xinzeng Wang

  4. GE Healthcare, New York, NY, USA

    Maggie Fung

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  1. Seok Hahn
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Correspondence to Jisook Yi.

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Author Jisook Yi has no conflict of interest.

Author Seok Hahn has no conflict of interest.

Author Ho-Joon Lee has no conflict of interest.

Author Yedaun Lee has no conflict of interest.

Author Joonsung Lee, Xinzeng Wang, Maggie Fung are employed by GE Healthcare and have no conflict of interest.

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Hahn, S., Yi, J., Lee, HJ. et al. Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction. Skeletal Radiol 52, 1545–1555 (2023). https://doi.org/10.1007/s00256-023-04321-8

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  • DOI: https://doi.org/10.1007/s00256-023-04321-8

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