Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction

Koichiro Yasaka; Shunichi Uehara; Shimpei Kato; Yusuke Watanabe; Taku Tajima; Hiroyuki Akai; Naoki Yoshioka; Masaaki Akahane; Kuni Ohtomo; Osamu Abe; Shigeru Kiryu

doi:10.1007/s10278-024-01112-y

Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction

J Imaging Inform Med. 2024 Apr 26. doi: 10.1007/s10278-024-01112-y. Online ahead of print.

Authors

Koichiro Yasaka^{1

2}, Shunichi Uehara¹, Shimpei Kato³, Yusuke Watanabe¹, Taku Tajima⁴, Hiroyuki Akai^{2

3}, Naoki Yoshioka², Masaaki Akahane², Kuni Ohtomo⁵, Osamu Abe¹, Shigeru Kiryu⁶

Affiliations

¹ Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
² Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
³ Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
⁴ Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan.
⁵ International University of Health and Welfare, 2600-1 Ktiakanemaru, Ohtawara, Tochigi, 324-8501, Japan.
⁶ Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan. kiryu-tky@umin.ac.jp.

PMID: 38671337
DOI: 10.1007/s10278-024-01112-y

Abstract

The aim of this study was to investigate whether super-resolution deep learning reconstruction (SR-DLR) is superior to conventional deep learning reconstruction (DLR) with respect to interobserver agreement in the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI. This retrospective study included 39 patients who underwent 1.5T cervical spine MRI. T2-weighted sagittal images were reconstructed with SR-DLR and DLR. Three blinded radiologists independently evaluated the images in terms of the degree of neuroforaminal stenosis, depictions of the vertebrae, spinal cord and neural foramina, sharpness, noise, artefacts and diagnostic acceptability. In quantitative image analyses, a fourth radiologist evaluated the signal-to-noise ratio (SNR) by placing a circular or ovoid region of interest on the spinal cord, and the edge slope based on a linear region of interest placed across the surface of the spinal cord. Interobserver agreement in the evaluations of neuroforaminal stenosis using SR-DLR and DLR was 0.422-0.571 and 0.410-0.542, respectively. The kappa values between reader 1 vs. reader 2 and reader 2 vs. reader 3 significantly differed. Two of the three readers rated depictions of the spinal cord, sharpness, and diagnostic acceptability as significantly better with SR-DLR than with DLR. Both SNR and edge slope (/mm) were also significantly better with SR-DLR (12.9 and 6031, respectively) than with DLR (11.5 and 3741, respectively) (p < 0.001 for both). In conclusion, compared to DLR, SR-DLR improved interobserver agreement in the evaluations of neuroforaminal stenosis using 1.5T cervical spine MRI.

Keywords: Artificial intelligence; Cervical vertebrae; Deep learning; Magnetic resonance imaging; Neurodegenerative diseases.