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Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes

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Abstract

Objectives

To investigate whether deep learning reconstruction (DLR) provides improved cervical spine MR images using a 1.5 T unit in the evaluation of degenerative changes without increasing imaging time.

Methods

This study included 21 volunteers (age 42.4 ± 11.9 years; 17 males) who underwent 1.5 T cervical spine sagittal T2-weighted MRI. From the imaging data with number of acquisitions (NAQ) of 1 or 2, images were reconstructed with DLR (NAQ1-DLR) and without DLR (NAQ1) or without DLR (NAQ2), respectively. Two readers evaluated the images for depiction of structures, artifacts, noise, overall image quality, spinal canal stenosis, and neuroforaminal stenosis. The two readers read studies blinded and randomly. Values were compared between NAQ1-DLR and NAQ1 and between NAQ1-DLR and NAQ2 using the Wilcoxon signed-rank test.

Results

The analyses showed significantly better results for NAQ1-DLR compared with NAQ1 and NAQ2 (p < 0.023), except for depiction of disc and foramina by one reader and artifacts by both readers in the comparison between NAQ1-DLR and NAQ2. Interobserver agreements (Cohen’s weighted kappa [97.5% confidence interval]) in the evaluation of spinal canal stenosis for NAQ1-DLR/NAQ1/NAQ2 were 0.874 (0.866–0.883)/0.778 (0.767–0.789)/0.818 (0.809–0.827), respectively, and those in the evaluation of neuroforaminal stenosis were 0.878 (0.872–0.883)/0.855 (0.849–0.860)/0.852 (0.845–0.860), respectively.

Conclusions

DLR improved the 1.5 T cervical spine MR images in the evaluation of degenerative spine changes.

Key Points

• Two radiologists demonstrated that deep learning reconstruction reduced the noise in cervical spine sagittal T2-weighted MR images obtained using a 1.5 T unit.

• Reduced noise in deep learning reconstruction images resulted in a clearer depiction of structures, such as the spinal cord, vertebrae, and zygapophyseal joint.

• Interobserver agreement in the evaluation of spinal canal stenosis and foraminal stenosis on cervical spine MR images was significantly improved using deep learning reconstruction (0.874 and 0.878, respectively) versus without deep learning (0.778–0.818 and 0.852–0.855, respectively).

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Abbreviations

CI:

Confidence interval

DLR:

Deep learning reconstruction

NAQ:

Number of acquisitions

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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.

Author information

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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  4. Taku Tajima
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  5. Hiroyuki Akai
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  6. Kuni Ohtomo
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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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Guarantor

The scientific guarantor of this publication is Shigeru Kiryu.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Shigeru Kiryu got research grants from Canon Medical Systems Corporation.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

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Yasaka, K., Tanishima, T., Ohtake, Y. et al. Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes. Eur Radiol 32, 6118–6125 (2022). https://doi.org/10.1007/s00330-022-08729-z

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  • DOI: https://doi.org/10.1007/s00330-022-08729-z

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