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Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction

  • Spinal Neuroradiology
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Abstract

Purpose

To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR.

Methods

In this prospective study, 21 volunteers (mean age: 42.4 ± 11.9 years; 17 males) underwent cervical spine T2-weighted sagittal 1.5T and 3T MRI on the same day. The 1.5T and 3T MRI data were used to reconstruct images with (1.5T-DLR) and without (3T-nonDLR) DLR, respectively. Regions of interest were marked on the spinal cord to calculate non-uniformity (NU; standard deviation/signal intensity × 100), as an indicator of image noise. Two blinded radiologists evaluated the images in terms of the depiction of structures, artifacts, noise, overall image quality, and neuroforaminal stenosis. The NU value and the subjective image quality scores were compared between 1.5T-DLR and 3T-nonDLR using the Wilcoxon signed-rank test. Interobserver agreement in evaluations of neuroforaminal stenosis for 1.5T-DLR and 3T-nonDLR was evaluated using Cohen’s weighted kappa analysis.

Results

The NU value for 1.5T-DLR was 8.4, which was significantly better than that for 3T-nonDLR (10.3; p < 0.001). Subjective image scores were significantly better for 1.5T-DLR than 3T-nonDLR images (p < 0.037). Interobserver agreement (95% confidence intervals) in the evaluations of neuroforaminal stenosis was significantly superior for 1.5T-DLR (0.920 [0.916–0.924]) than 3T-nonDLR (0.894 [0.889–0.898]).

Conclusion

By using DLR, image quality and interobserver agreement in evaluations of neuroforaminal stenosis on 1.5T cervical spine MRI could be improved compared to 3T MRI without DLR.

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Funding

This study was financially supported by Canon Medical Systems Corporation. Any data and information included in this study was not controlled by Canon Medical Systems Corporation.

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Authors and Affiliations

  1. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Koichiro Yasaka, Tomoya Tanishima, Yuta Ohtake & Osamu Abe

  2. Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba, 286-8520, Japan

    Koichiro Yasaka, Hiroyuki Akai & Shigeru Kiryu

  3. Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan

    Taku Tajima

  4. Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

    Hiroyuki Akai

  5. International University of Health and Welfare, 2600-1 Kitakanamaru, Otawara, Tochigi, 324-8501, Japan

    Kuni Ohtomo

Authors
  1. Koichiro Yasaka
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  2. Tomoya Tanishima
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  3. Yuta Ohtake
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  7. Osamu Abe
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  8. Shigeru Kiryu
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Corresponding author

Correspondence to Shigeru Kiryu.

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Conflict of interest

The author declares a conflict of interest: Shigeru Kiryu got research grants from Canon Medical Systems Corporation.

Ethical approval

This prospective study was approved by our institutional review board (20-Nr-059).

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Written informed consent was obtained from all study participants.

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Yasaka, K., Tanishima, T., Ohtake, Y. et al. Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction. Neuroradiology 64, 2077–2083 (2022). https://doi.org/10.1007/s00234-022-03024-6

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  • DOI: https://doi.org/10.1007/s00234-022-03024-6

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