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Journal of Imaging Informatics in Medicine

26-04-2024 | Magnetic Resonance Imaging of the Spine | Original Paper

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

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

Published in: Journal of Imaging Informatics in Medicine

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.

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Title: Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction
Authors: Koichiro Yasaka
Shunichi Uehara
Shimpei Kato
Yusuke Watanabe
Taku Tajima
Hiroyuki Akai
Naoki Yoshioka
Masaaki Akahane
Kuni Ohtomo
Osamu Abe
Shigeru Kiryu
Publication date: 26-04-2024
Publisher: Springer International Publishing
Keyword: Magnetic Resonance Imaging of the Spine
Published in: Journal of Imaging Informatics in Medicine
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-024-01112-y

At a glance: The STEP trials

Springer Medicine

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

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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