RESUMEN

INTRODUCTION: Magnetic Resonance Imaging (MRI) has revolutionized the diagnosis and treatment of musculoskeletal disorders. Parallel imaging (PI) and compressed sensing (CS) techniques reduce scan time, but higher acceleration factors decrease image quality. Artificial intelligence has enhanced MRI reconstructions by integrating deep learning algorithms. Therefore, the study aims to review the impact of Artificial intelligence-assisted compressed sensing (AI-CS) and acceleration factors on scan time and image quality in musculoskeletal MRI. METHODS: Database searches were completed across PubMed, Scopus, CINAHL, Web of Science, Cochrane Library, and Embase to identify relevant articles focusing on the application of AI-CS in musculoskeletal MRI between 2022 and 2024. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines to extract data from the selected studies. RESULTS: Nine articles were included for the final review, with a total sample size of 730 participants. Of these, seven articles were rated as high, while two articles were considered to be of moderate quality. MRI examination with AI-CS showed scan time reduction of 18.9-38.8% for lumbar spine, 38-40% for shoulder, 54-75% for knee and 53-63% for ankle. CONCLUSIONS: AI-CS showed a significant reduction in scan time and improved image quality for 2D and 3D sequences in musculoskeletal MRI compared with PI and CS. Determining the optimal acceleration factor necessary to achieve images with higher image quality compared to traditional PI techniques is required before clinical implementation. Higher acceleration factors currently lead to reduced image scores, although advancements in AI-CS are expected to address the limitation. IMPLICATIONS OF PRACTICE: AI-CS in MRI improves patient care by shortening scan times, reducing patient discomfort and anxiety, and produces high quality images for accurate diagnosis.