Hyperelastic meniscal material characterization via inverse parameter identification for knee arthroscopic simulations

Abstract

Understanding the complex behavior of menisci is of growing interest in many fields including sports medicine, surgical simulation, and implant design. The selection of an appropriate material model and accurate model parameters contribute to identifying the degree of degeneration of the meniscus. Incorporating patient-specific material parameters could further improve the safe handling of tissue during probing in knee arthroscopy simulations, supporting more informed intraoperative decision-making. The objective of this study is to identify hyperelastic material parameters of individual human menisci based on an inverse parameter identification approach using optimization and demonstrate a real-time interactive surgical simulation using identified parameters. Mechanical tests were conducted in indentation of the anterior, mid-body, and posterior regions of five lateral and medial menisci to obtain experimental force–displacement data. An inverse parameter identification based on these tests and finite element (FE) models was employed to minimize the differences between the experimental and simulated force. The region-specific FE models considered the predominant collagen fiber orientation of the meniscus. Anisotropic hyperelastic material parameters were optimized using a particle swarm optimization algorithm. Finally, the optimized parameters were used in simulation open framework architecture (SOFA) and demonstrated a real-time probe-meniscus interaction during the arthroscopic meniscus examination. The optimized values revealed subject-specific characteristics, along with anatomical and regional variations, with high shear modulus observed in the anterior region of the medial meniscus (0.76 ± 0.28 MPa for 1 mm indentation). Additionally, an increase in shear modulus was observed with increased indentation depth ($p<0.05$ except for the mid-body of the medial meniscus).