Meghan Jardon1, Ek T Tan1, J Levi Chazen1, Meghan Sahr1, Yan Wen2, Brandon Schneider3, Darryl B Sneag4. 1. Department of Radiology and Imaging, Hospital for Special Surgery, 535 E 70th St, New York, NY, 10021, USA. 2. GE Healthcare, Waukesha, WI, USA. 3. Biostatistics Core, Research Administration, Hospital for Special Surgery, New York, NY, 10021, USA. 4. Department of Radiology and Imaging, Hospital for Special Surgery, 535 E 70th St, New York, NY, 10021, USA. sneagd@hss.edu.
Abstract
OBJECTIVE: To compare standard-of-care two-dimensional MRI acquisitions of the cervical spine with those from a single three-dimensional MRI acquisition, reconstructed using a deep-learning-based reconstruction algorithm. We hypothesized that the improved image quality provided by deep-learning-based reconstruction would result in improved inter-rater agreement for cervical spine foraminal stenosis compared to conventional two-dimensional acquisitions. MATERIALS AND METHODS: Forty-one patients underwent routine cervical spine MRI with a conventional protocol comprising two-dimensional T2-weighted fast spin echo scans (2 axial planes, 1 sagittal plane), and an isotropic-resolution three-dimensional T2-weighted fast spin echo scan reconstructed over a 4-h time window with a deep-learning-based reconstruction algorithm. Three radiologists retrospectively assessed images for the degree to which motion artifact limited clinical assessment, and foraminal and central stenosis at each level. Inter-rater agreement was analyzed with weighted Fleiss's kappa (k) and comparisons between two-dimensional and three-dimensional sequences were performed with Wilcoxon signed-rank test. RESULTS: Inter-rater agreement for foraminal stenosis was "substantial" for two-dimensional sequences (k = 0.76) and "excellent" for the three-dimensional sequence (k = 0.81). Agreement was "excellent" for both sequences (k = 0.85 and 0.83) for central stenosis. The three-dimensional sequence had less perceptible motion artifact (p ≤ 0.001-0.036). Mean total scan time was 10.8 min for the two-dimensional sequences, and 7.3 min for the three-dimensional sequence. CONCLUSION: Three-dimensional MRI reconstructed with a deep-learning-based algorithm provided "excellent" inter-observer agreement for foraminal and central stenosis, which was at least equivalent to standard-of-care two-dimensional imaging. Three-dimensional MRI with deep-learning-based reconstruction was less prone to motion artifact, with overall scan time savings.
OBJECTIVE: To compare standard-of-care two-dimensional MRI acquisitions of the cervical spine with those from a single three-dimensional MRI acquisition, reconstructed using a deep-learning-based reconstruction algorithm. We hypothesized that the improved image quality provided by deep-learning-based reconstruction would result in improved inter-rater agreement for cervical spine foraminal stenosis compared to conventional two-dimensional acquisitions. MATERIALS AND METHODS: Forty-one patients underwent routine cervical spine MRI with a conventional protocol comprising two-dimensional T2-weighted fast spin echo scans (2 axial planes, 1 sagittal plane), and an isotropic-resolution three-dimensional T2-weighted fast spin echo scan reconstructed over a 4-h time window with a deep-learning-based reconstruction algorithm. Three radiologists retrospectively assessed images for the degree to which motion artifact limited clinical assessment, and foraminal and central stenosis at each level. Inter-rater agreement was analyzed with weighted Fleiss's kappa (k) and comparisons between two-dimensional and three-dimensional sequences were performed with Wilcoxon signed-rank test. RESULTS: Inter-rater agreement for foraminal stenosis was "substantial" for two-dimensional sequences (k = 0.76) and "excellent" for the three-dimensional sequence (k = 0.81). Agreement was "excellent" for both sequences (k = 0.85 and 0.83) for central stenosis. The three-dimensional sequence had less perceptible motion artifact (p ≤ 0.001-0.036). Mean total scan time was 10.8 min for the two-dimensional sequences, and 7.3 min for the three-dimensional sequence. CONCLUSION: Three-dimensional MRI reconstructed with a deep-learning-based algorithm provided "excellent" inter-observer agreement for foraminal and central stenosis, which was at least equivalent to standard-of-care two-dimensional imaging. Three-dimensional MRI with deep-learning-based reconstruction was less prone to motion artifact, with overall scan time savings.
Authors: D L Daniels; J S Hyde; J B Kneeland; A Jesmanowicz; W Froncisz; T M Grist; P Pech; A L Williams; V M Haughton Journal: AJNR Am J Neuroradiol Date: 1986 Jan-Feb Impact factor: 3.825