In this study we evaluate the viability of using ZTE, a novel MRI sequence for bone imaging, with deep-learning (DL) reconstruction to assess glenohumeral shoulder instability, aiming to improve signal-to-noise ratio (SNR) and get comparable images to CT, the gold standard technique for surgical planning. Bone loss measurements were performed on both techniques achieving almost perfect inter-modality agreement on 20 patients. This approach could prevent the patient from receiving ionizing radiation concomitant to CT examination and could be combined in a single routine shoulder examination with other MR sequences for a complete study and optimized patient workflow.
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