Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation to date.The objective of this study was to evaluate the inter-reader agreement of conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep-learning super-resolution (DLSR) augmentation, as well as to compare the diagnostic performance of the two methods with respect to findings from arthroscopic surgery.A total of 51 patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective DLSR to enhance qDESS slice-resolution twofold. A musculoskeletal radiologist and a radiology resident performed retrospective independent evaluations of the articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a two-month washout period, the readers reviewed qDESS images alone, followed by qDESS with the automatic T2 maps. Inter-reader agreement between conventional MRI and qDESS was computed using percent agreement and Cohen's Kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS+T2 were compared with arthroscopic findings using exact McNemar's tests.Conventional MRI and qDESS demonstrated 92% agreement in evaluation of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium combined. Kappa was 0.79 (0.76-0.81) across all imaging findings. In the 43/51 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p=0.23-1.00) between conventional MRI (sensitivity: 58%-93%; specificity: 27%-87%) and qDESS alone (sensitivity: 54%-90%; specificity: 23%-91%) for cartilage, menisci, ligaments, and synovium. Sensitivity and specificity for grade 1 cartilage lesions were 33%/56% for conventional MRI, 23%/53% for qDESS (p=0.81), and 46%/39% for qDESS+T2 (p=0.80); for grade 2A lesions, 27%/53% for conventional MRI, 26%/52% for qDESS (p=0.02), and 58%/40% for qDESS+T2 (p<0.001).qDESS prospectively enhanced with deep learning had strong inter-reader agreement with conventional knee MRI and near-equivalent diagnostic performance with respect to arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. Clinical Impact: qDESS using prospective artificial intelligence image quality enhancement may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.
View details for DOI 10.2214/AJR.20.24172
View details for PubMedID 32755384