Computational Knee Ligament Modeling Using Experimentally Determined Zero-Load Lengths

Abstract

This study presents a subject-specific method of determining the zero-load lengths of the cruciate and collateral ligaments in computational knee modeling. Three cadaver knees were tested in a dynamic knee simulator. The cadaver knees also underwent manual envelope of motion testing to find their passive range of motion in order to determine the zero-load lengths for each ligament bundle. Computational multibody knee models were created for each knee and model kinematics were compared to experimental kinematics for a simulated walk cycle. One-dimensional non-linear spring damper elements were used to represent cruciate and collateral ligament bundles in the knee models. This study found that knee kinematics were highly sensitive to altering of the zero-load length. The results also suggest optimal methods for defining each of the ligament bundle zero-load lengths, regardless of the subject. These results verify the importance of the zero-load length when modeling the knee joint and verify that manual envelope of motion measurements can be used to determine the passive range of motion of the knee joint. It is also believed that the method described here for determining zero-load length can be used for in vitro or in vivo subject-specific computational models.

Estimating patient‐specific soft‐tissue properties in a TKA knee

“…The RMS errors for simulations based on optimized properties are small enough that those simulations should be able to detect clinically significant changes in tibiofemoral kinematics, which the Knee Society Scoring System identifies as 6°, while RMS errors for generic properties are large enough to affect the clinical interpretation of the results. The RMS errors for our simulations using optimized properties were similar to those found by other researchers, who estimated subject‐specific soft‐tissue properties by matching kinematics from a mechanical knee simulator and found RMS errors of between 0.6° and 2.6° when comparing internal/external and varus/valgus angles, and found RMS errors of between 0.99° and 7.1° when comparing the orientation of the tibia coordinate system relative to the femoral coordinate system . The results for 20° and 90° of flexion (Fig.…”

“…A model containing only the collateral ligaments was able to reproduce the varus/valgus behavior, but could not fully capture the internal/external behavior until additional ligaments (ALL, PFL, PMC, dMCL) were added, which is consistent with previous studies . While others have directly digitized ligament attachment points in their simulations, a preliminary sensitivity analysis of our model that showed that tibiofemoral kinematics were not sensitive to ligament attachment points. Therefore, attachment points for each ligament were taken from literature rather than measured off individual specimens.…”

Preliminary study of a customised total knee implant with musculoskeletal and dynamic squatting simulation

“…First, the translational DOFs of the hip joint were constrained in the FE model, while the translational DOFs of the ankle joint were constrained in the OKR [17][18][19] and KKS. [10][11][12][13][14] Second, 23 lower limb muscles including hamstrings and tibialis anterior muscles were simulated in the FE model with specific locations of muscle insertions, while only quadriceps were considered and simplified as an elastic strap driven by a servomotor in the OKR and KKS. Third, time-varying and vertical ankle joint loads were applied to the FE model in this study for considering the effect of ground reaction forces during the squatting motion.…”

“…Once the coefficients B and D have been solved through equations (11) and (12) by substituting the coefficients C and E, the fitting elliptical equation can be obtained as equation 13x…”

“…However, in their experiments, the effect of coordinated muscle forces was neglected, and close-tophysiological hip and ankle joint forces were not applied. In fact, these two problems are common in assessment of TKI performance by either computer simulation [10][11][12][13][14] or in vitro experiment [15][16][17] based on the OKR 18,19 or the Kansas Knee Simulator (KKS). 20,21 In reality, muscle forces do play an important role in joint motion because medium to high quadriceps, hamstrings and gastrocnemius activities are produced during squatting.…”