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- W4385547584 abstract "Isotropic 3D MRI, with the addition of deep-learning based reconstruction algorithms (DLRecon), facilitates faster acquisition times and multiplanar reformatting. We compared interobserver agreement for cervical spine neural foraminal (NF) stenosis assessment of 3D T2-weighted fast spin echo (T2w-FSE) MR images with DLRecon to those of standard-of-care (SOC) 2D T2w-FSE images. We demonstrated that inter-observer agreement was high for both 2D and 3D sequences in the assessment of NF stenosis, but agreement was more consistent between readers at each level for the 3D acquisition. 3D DLRecon images were also more frequently free of motion, when compared to corresponding 2D sequences." @default.
- W4385547584 created "2023-08-04" @default.
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- W4385547584 date "2023-08-03" @default.
- W4385547584 modified "2023-09-27" @default.
- W4385547584 title "Evaluation of Deep-Learning Reconstructed High-Resolution 3D Cervical Spine MRI to Improve Foraminal Stenosis Evaluation" @default.
- W4385547584 doi "https://doi.org/10.58530/2022/2889" @default.
- W4385547584 hasPublicationYear "2023" @default.
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