Fig. 1—

Flowchart shows patient inclusion (left) and paradigm for training deep learning super-resolution (DLSR) algorithm (right). Super-resolution method was initially pretrained using 176 double-echo steady-state (DESS) scans with combined echoes from Osteoarthritis Initiative (OAI) [21]. Patients (n = 301) underwent quantitative DESS (qDESS). Imaging from 49 of these patients was used for subsequent fine-tuning of pretrained super-resolution method, and remaining 252 patients were eligible for study inclusion. Of those 252 patients, 51 underwent surgery and were included in final sample. Conventional MR and Qdess images from these patients were evaluated by two radiologists, with 2-month washout period between interpretations. Of 51 patients, 43 had arthroscopic reports with complete information for evaluating cartilage, menisci, ligaments, and synovium.