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Abstract #2889

Evaluation of Deep-Learning Reconstructed High-Resolution 3D Cervical Spine MRI to Improve Foraminal Stenosis Evaluation

Meghan Jardon1, Ek T. Tan1, J. Levi Chazen1, Meghan Sahr1, Yan Wen2, Alyssa M. Vanderbeek3, and Darryl B. Sneag1
1Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States, 2GE Healthcare, Chicago, IL, United States, 3Biostatistics Core, Research Administration, Hospital for Special Surgery, New York, NY, United States


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.

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