Analysis of Low-Field Magnetic Resonance Imaging Scanners for Evaluation of Knee Pathology Based on Arthroscopy

基于关节镜检查的低场磁共振成像扫描仪在膝关节病变评估中的应用分析

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Abstract

BACKGROUND: In recent years, few studies have evaluated low-field magnetic resonance imaging (MRI) diagnoses compared with intraoperative findings of the knee. PURPOSE: To determine the accuracy and sensitivity of low-field MRI scanners in diagnosing pathology of the menisci, cruciate ligaments, and osteochondral surfaces. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: MRI examinations without intra-articular contrast were performed on 379 patients for knee pathologies over a 4-year period. The MRI examinations were done using a 0.2-tesla scanner utilizing a dedicated knee coil and read by 1 of 3 board-certified, musculoskeletal fellowship-trained radiologists. Within a mean time of 50 days after MRI, all patients underwent knee arthroscopy performed by 1 of 2 sports fellowship-trained orthopaedic surgeons. Operative notes from the knee arthroscopies were then reviewed by a single independent observer, and the intraoperative findings were compared with the MRI reports. RESULTS: For medial meniscus tears, the sensitivity, specificity, positive predictive value, and negative predictive value were 83%, 81%, 89%, and 71%, respectively. For lateral meniscus tears, the values were 51%, 93%, 84%, and 73%, respectively. For anterior cruciate ligament (ACL) tears, the values were 85%, 94%, 69%, and 97%, respectively. For osteochondral lesions, the values were 8%, 99%, 29%, and 94%, respectively. For posterior cruciate ligament (PCL) tears, the specificity and negative predictive value were 99% and 100%, respectively. CONCLUSION: Low-field MRI was an accurate tool for evaluation of medial meniscus and ACL tears. However, within the study population, it is not as effective in diagnosing lateral meniscus tears and showed a poor ability to detect osteochondral lesions. More information is needed to properly assess its ability to diagnose PCL tears.

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