Literature DB >> 35918450

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

Koichiro Yasaka1,2, Tomoya Tanishima1, Yuta Ohtake1, Taku Tajima3, Hiroyuki Akai2,4, Kuni Ohtomo5, Osamu Abe1, Shigeru Kiryu6.   

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.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

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

Mesh:

Year:  2022        PMID: 35918450     DOI: 10.1007/s00234-022-03024-6

Source DB:  PubMed          Journal:  Neuroradiology        ISSN: 0028-3940            Impact factor:   2.995


  2 in total

1.  Cervical spondylosis: recognition, differential diagnosis, and management.

Authors:  R M Voorhies
Journal:  Ochsner J       Date:  2001-04

2.  Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI.

Authors:  Nobuo Kashiwagi; Hisashi Tanaka; Yuichi Yamashita; Hiroto Takahashi; Yoshimori Kassai; Masahiro Fujiwara; Noriyuki Tomiyama
Journal:  Acta Radiol Open       Date:  2021-06-18
  2 in total

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