Quantitative fat-fraction analysis of the rotator cuff muscles on clinical sagittal and coronal T1-weighted MRI using deep learning algorithms.

Increased fatty infiltration of the rotator cuff muscles is a primary prognostic factor for poor surgical outcomes of rotator cuff repair surgery. Preoperative fat assessment currently relies on the qualitative Goutallier classification using magnetic resonance imaging (MRI). This method suffers from high observer variability and only assesses a single slice. The aim of this study was to use deep learning to predict quantitative, voxel-wise fat fraction (FF) from standard T1-weighted MRIs. A deep learning-based algorithm was developed for automatic FF prediction using a voxel-wise, five-class system. The network was trained on 75 patients using paired T1-weighted and 2-point Dixon MRI, with rotator cuff muscles segmented in coronal and sagittal planes. It was validated on 24 patients. The proposed algorithm was significantly more accurate than a binary fat classification approach (p < 0.001). Average whole muscle FF calculation errors (mean ± standard deviation) ranged from - 0.5 ± 2.2% to 2.3 ± 3.9% compared to Dixon MRI measures. Deep learning enabled an accurate, voxel-wise FF quantification using clinical T1-weighted MRIs. This method allows for muscle FF distribution analysis, providing a more comprehensive assessment, that can improve prognosis analysis and optimise treatment planning.