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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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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.


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.


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.


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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Confidence interval


Deep learning reconstruction


Number of acquisitions


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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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Correspondence to Shigeru Kiryu.

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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.

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Institutional review board approval was obtained.


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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).

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