RADIOLOGICAL VISUALIZATION AND ANALYSIS OF ANTEROLATERAL LIGAMENT: A CLINICAL IMAGING STUDY

Abstract Background: The anterolateral ligament (ALL) of the knee is an important secondary stabilizer against anterolateral rotatory instability. However, its depiction on conventional Magnetic Resonance Imaging (MRI) has been inconsistent, limiting its clinical utility. This study aimed to validate an optimized 3.0 Tesla (3.0T) MRI protocol for consistent visualization of the ALL, perform morphometric analysis, and assess its diagnostic reliability as an independent imaging technique. Methods: This cross-sectional, descriptive study analyzed 100 consecutive knee MRI examinations obtained using a 3.0Tesla Philips Achieva. The imaging protocol incorporated a high-resolution 3D Proton Density (PD) VISTA sequence with 0.7 mm isotropic voxels. Multiplanar reformation (MPR) was standardized to align with the ALL’s anatomical axis. Each scan was evaluated for ligament visibility, morphology, and morphometric parameters including length, width, and thickness. Inter-observer reliability was calculated using the Intraclass Correlation Coefficient (ICC). Results: The optimized protocol achieved a 90.0% (90/100 knees) ALL identification rate, with 68.0% fully and 22.0% partially visualized. The PD-FS sequence provided the highest detection rate (92%). The tibial insertion was the most consistently visualized portion (95.0%). Morphometric assessment yielded reproducible values across observers. Inter-observer reliability was excellent for all parameters, with ICC values between 0.82 and 0.91 (p < 0.001). Conclusion: A standardized 3.0T MRI protocol employing high-resolution 3D sequences and MPR alignment offers reliable and reproducible assessment of the anterolateral ligament. This approach ensures consistent visualization and precise morphometric evaluation, supporting MRI as a dependable, non-invasive modality for integrating ALL assessment into the diagnostic evaluation of complex knee instability and enhancing preoperative planning.