Comparison of Deep-Learning-Based Reconstruction of Knee MRI with Conventional Sequences in Patients with Knee Pain

Abstract

Background: Knee MRI is one of the most performed MR exams in outpatient settings and has a crucial role in detecting pathology. To increase access to advanced imaging and provide valuable diagnostic information to the clinician, reducing imaging acquisition time while preserving good image qualities is crucial. Deep learning-based reconstruction (DLR) algorithm (SubtleMR™) may be an alternative to conventional MRI with reduced scanning time while preserving image quality and diagnostic value. To compare image quality and diagnostic performance among conventional sequences and an automated accelerated deep learning-based reconstruction (DLR) knee algorithm method in patients with knee pain. Material and Methods: A total of 50 consecutive patients (26 men, mean age of 43.5 ± 10 years) with knee pain who underwent conventional (acquisition time: 13 min and 27 s) and accelerated DLR (acquisition time: 6 min and 45 s) knee MRI between November 2020 and December 2020 on a 1.5 T MRI system were prospectively enrolled. Two independent musculoskeletal radiologists compared the image quality artifacts and pathology using a 3-point Likert scale (1–3). Data were compared using paired Wilcoxon signed rank tests. Results: Forty-three patients with 86 knee MRI examinations were included. A comparison of conventional sequences and accelerated DLR by both readers revealed that there were no significant differences in overall diagnosis, image quality, and artifacts. Accelerated DLR knee MRI is comparable with conventional acquisitions, with an average time difference of 6 min and 42 s per patient (49.81%). Conclusions: Accelerated DLR knee MRI substantially reduces acquisition time (~49.81%) while maintaining equivalent diagnosis and image quality.