Development of a knee joint magnetic resonance imaging (MRI)-based model for finite element analysis (FEA) applications.

Abstract

<h4>Introduction</h4>The knee is a biomechanically complex joint supported by multiple anatomical structures, making it vulnerable to multiple injuries. Finite element analysis is a valuable tool for studying joint biomechanics, particularly in pre-operative planning and injury evaluation. However, most models are based on computed tomography, which limits soft tissue visualization. Thus, a magnetic resonance imaging-based finite element model of the knee, incorporating bones, ligaments, tendons, cartilage, and menisci, was developed to improve realism and clinical relevance in biomechanical simulations.<h4>Materials and methods</h4>Magnetic resonance imaging data were obtained from a healthy adult male using a 1.5T scanner and processed using RETOMO and Rhinoceros software for 3D reconstruction and modeling. Meshes were cleaned, optimized, and anatomically validated. All major knee structures were modeled, including the femur, tibia, fibula, patella, cruciate and collateral ligaments, patellofemoral ligaments, quadriceps and patellar tendons, menisci, and articular cartilage.<h4>Results</h4>The resulting model reconstructed both hard and soft tissues of the knee joint with high anatomical fidelity, based on direct MRI segmentation and literature-supported anatomical definitions. The use of magnetic resonance imaging enabled high-resolution identification of soft tissues, while advanced mesh refinement preserved anatomical detail with optimized file management. The inclusion of structures like the anterolateral ligament and patellofemoral ligaments expands the model's clinical relevance in addressing a wider range of knee pathologies.<h4>Conclusion</h4>This magnetic resonance imaging-based finite element analysis model provides a detailed and comprehensive, representation of the healthy human knee, including bones, cartilage, menisci, and tendons. While some ligament attachment points were derived from literature rather than MRI data, the model provides a foundation for future biomechanical studies, surgical planning and personalized treatment simulations.