Mohammad Kia

A subject specific multibody model of the knee with menisci

The menisci of the knee play an important role in joint function and our understanding of knee mechanics and tissue interactions can be enhanced through computational models of the tibio-menisco-femoral structure. Several finite element models of the knee that include meniscus-cartilage contact exist, but these models are typically limited to simplified boundary conditions. Movement simulation and musculoskeletal modeling can predict muscle forces, but are typically performed using the multibody method with simplified representation of joint structures. This study develops a subject specific computational model of the knee with menisci that can be incorporated into neuromusculoskeletal models within a multibody framework. Meniscus geometries from a 78-year-old female right cadaver knee were divided into 61 discrete elements (29 medial and 32 lateral) that were connected through 6x6 stiffness matrices. An optimization and design of experiments approach was used to determine parameters for the 6x6 stiffness matrices such that the force-displacement relationship of the meniscus matched that of a linearly elastic transversely isotropic finite element model for the same cadaver knee. Similarly, parameters for compliant contact models of tibio-menisco-femoral articulations were derived from finite element solutions. As a final step, a multibody knee model was developed and placed within a dynamic knee simulator model and the tibio-femoral and patello-femoral kinematics compared to an identically loaded cadaver knee. RMS errors between finite element displacement and multibody displacement after parameter optimization were 0.017 mm for the lateral meniscus and 0.051 mm for the medial meniscus. RMS errors between model predicted and experimental cadaver kinematics during a walk cycle were less than 11 mm translation and less than 7 degrees orientation. A small improvement in kinematics, compared to experimental measurements, was seen when the menisci were included versus a model without the menisci. With the menisci the predicted tibio-femoral contact force was significantly reduced on the lateral side (937 N peak force versus 633 N peak force), but no significant reduction was seen on the medial side.

Evaluation of a musculoskeletal model with prosthetic knee through six experimental gait trials

Knowledge of the forces acting on musculoskeletal joint tissues during movement benefits tissue engineering, artificial joint replacement, and our understanding of ligament and cartilage injury. Computational models can be used to predict these internal forces, but musculoskeletal models that simultaneously calculate muscle force and the resulting loading on joint structures are rare. This study used publicly available gait, skeletal geometry, and instrumented prosthetic knee loading data [1] to evaluate muscle driven forward dynamics simulations of walking. Inputs to the simulation were measured kinematics and outputs included muscle, ground reaction, ligament, and joint contact forces. A full body musculoskeletal model with subject specific lower extremity geometries was developed in the multibody framework. A compliant contact was defined between the prosthetic femoral component and tibia insert geometries. Ligament structures were modeled with a nonlinear force-strain relationship. The model included 45 muscles on the right lower leg. During forward dynamics simulations a feedback control scheme calculated muscle forces using the error signal between the current muscle lengths and the lengths recorded during inverse kinematics simulations. Predicted tibio-femoral contact force, ground reaction forces, and muscle forces were compared to experimental measurements for six different gait trials using three different gait types (normal, trunk sway, and medial thrust). The mean average deviation (MAD) and root mean square deviation (RMSD) over one gait cycle are reported. The muscle driven forward dynamics simulations were computationally efficient and consistently reproduced the inverse kinematics motion. The forward simulations also predicted total knee contact forces (166N<MAD<404N, 212N<RMSD<448N) and vertical ground reaction forces (66N<MAD<90N, 97N<RMSD<128N) well within 28% and 16% of experimental loads, respectively. However the simplified muscle length feedback control scheme did not realistically represent physiological motor control patterns during gait. Consequently, the simulations did not accurately predict medial/lateral tibio-femoral force distribution and muscle activation timing.

Kinematics of passive flexion following balanced and overstuffed fixed bearing unicondylar knee arthroplasty

INTRODUCTION Progression of osteoarthritis in the unreplaced compartment following unicondylar knee arthroplasty (UKA) may be hastened if kinematics is disturbed following UKA implantation. The purpose of this study was to analyze tibiofemoral kinematics of the balanced and overstuffed UKA in comparison with the native knee during passive flexion since this is a common clinical assessment. METHODS Ten cadaveric knees were mounted to robotic manipulator and underwent passive flexion from 0 to 90°. The kinematic pathway was recorded in the native knee and in the balanced, fixed bearing UKA. The medial UKA was implanted using a measured resection technique. Additionally, a one millimeter thicker tibial insert was installed to simulate the effects of overstuffing. Tibial kinematics in relation to the femur was recorded. RESULTS Following UKA the tibia was externally rotated, and in valgus relative to the native knee near extension. In flexion, installing the UKA caused the knee to be translated medially and anteriorly. The tibia was translated distally through the entire range of flexion after UKA. Compared to the balanced UKA, overstuffing further increased valgus at full extension and distal translation of the tibia from full extension to 45° flexion. CONCLUSIONS UKA implantation altered tibiofemoral kinematics in all planes. Differences were small; nevertheless, they may affect tibiofemoral loading patterns. CLINICAL RELEVANCE Alterations in tibiofemoral kinematics following UKA might have implications for prosthesis failure and progression of osteoarthritis in the remaining compartment. Overstuffing should be avoided as it further increased valgus and did not improve the remaining kinematics.

A Multibody Knee Model Corroborates Subject-Specific Experimental Measurements of Low Ligament Forces and Kinematic Coupling During Passive Flexion

A multibody model of the knee was developed and the predicted ligament forces and kinematics during passive flexion corroborated subject-specific measurements obtained from a human cadaveric knee that was tested using a robotic manipulator. The model incorporated a novel strategy to estimate the slack length of ligament fibers based on experimentally measured ligament forces at full extension and included multifiber representations for the cruciates. The model captured experimentally measured ligament forces (≤ 5.7 N root mean square (RMS) difference), coupled internal rotation (≤ 1.6 deg RMS difference), and coupled anterior translation (≤ 0.4 mm RMS difference) through 130 deg of passive flexion. This integrated framework of model and experiment improves our understanding of how passive structures, such as ligaments and articular geometries, interact to generate knee kinematics and ligament forces.

Validation of Computational Knee Models Using a Dynamic Knee Simulator

Computational models of the knee provide valuable information on knee biomechanics, but validation of these models is challenging as in-vivo parameters such as muscle forces and tissue loading cannot be measured. Machines that simulate the dynamic loading and motion of physiological activities on cadaver knees can provide a means for validating computational knee models and modeling methods. In this approach, all forces applied to cadaver knees are known and can be replicated in computational simulations. The resulting experimental and computational kinematics can then be compared. Presented here is the development and use of a modeling platform comprised of a multi-body computational model of a cadaver knee and dynamic knee simulator and experimental measurements from the cadaver knee loaded in the machine. This modeling platform has been used to study: 1) patient specific reference lengths versus literature obtained reference lengths [1], 2) inclusion of ligament and tendon wrapping [2] and, 3) the development of a multi-body model of the meniscus [3].Copyright

Fixed-bearing medial unicompartmental knee arthroplasty restores neither the medial pivoting behavior nor the ligament forces of the intact knee in passive flexion: MEDIAL UNICOMPARTMENTAL KNEE ARTHROPLASTY

Medial unicompartmental knee arthroplasty (UKA) is an accepted treatment for isolated medial osteoarthritis. However, using an improper thickness for the tibial component may contribute to early failure of the prosthesis or disease progression in the unreplaced lateral compartment. Little is known of the effect of insert thickness on both knee kinematics and ligament forces. Therefore, a computational model of the tibiofemoral joint was used to determine how non‐conforming, fixed bearing medial UKA affects tibiofemoral kinematics, and tension in the medial collateral ligament (MCL) and the anterior cruciate ligament (ACL) during passive knee flexion. Fixed bearing medial UKA could not maintain the medial pivoting that occurred in the intact knee from 0° to 30° of passive flexion. Abnormal anterior–posterior (AP) translations of the femoral condyles relative to the tibia delayed coupled internal tibial rotation, which occurred in the intact knee from 0° to 30° of flexion, but occurred from 30° to 90° of flexion following UKA. Increasing or decreasing tibial insert thickness following medial UKA also failed to restore the medial pivoting behavior of the intact knee despite modulating MCL and ACL forces. Reduced AP constraint in non‐conforming medial UKA relative to the intact knee leads to abnormal condylar translations regardless of insert thickness even with intact cruciate and collateral ligaments. This finding suggests that the conformity of the medial compartment as driven by the medial meniscus and articular morphology plays an important role in controlling AP condylar translations in the intact tibiofemoral joint during passive flexion.

Femoral Component External Rotation Affects Knee Biomechanics: A Computational Model of Posterior-stabilized TKA

Background The correct amount of external rotation of the femoral component during TKA is controversial because the resulting changes in biomechanical knee function associated with varying degrees of femoral component rotation are not well understood. We addressed this question using a computational model, which allowed us to isolate the biomechanical impact of geometric factors including bony shapes, location of ligament insertions, and implant size across three different knees after posterior-stabilized (PS) TKA. Questions/purposes Using a computational model of the tibiofemoral joint, we asked: (1) Does external rotation unload the medial collateral ligament (MCL) and what is the effect on lateral collateral ligament tension? (2) How does external rotation alter tibiofemoral contact loads and kinematics? (3) Does 3° external rotation relative to the posterior condylar axis align the component to the surgical transepicondylar axis (sTEA) and what anatomic factors of the femoral condyle explain variations in maximum MCL tension among knees? Methods We incorporated a PS TKA into a previously developed computational knee model applied to three neutrally aligned, nonarthritic, male cadaveric knees. The computational knee model was previously shown to corroborate coupled motions and ligament loading patterns of the native knee through a range of flexion. Implant geometries were virtually installed using hip-to-ankle CT scans through measured resection and anterior referencing surgical techniques. Collateral ligament properties were standardized across each knee model by defining stiffness and slack lengths based on the healthy population. The femoral component was externally rotated from 0° to 9° relative to the posterior condylar axis in 3° increments. At each increment, the knee was flexed under 500 N compression from 0° to 90° simulating an intraoperative examination. The computational model predicted collateral ligament forces, compartmental contact forces, and tibiofemoral internal/external and varus-valgus rotation through the flexion range. Results The computational model predicted that femoral component external rotation relative to the posterior condylar axis unloads the MCL and the medial compartment; however, these effects were inconsistent from knee to knee. When the femoral component was externally rotated by 9° rather than 0° in knees one, two, and three, the maximum force carried by the MCL decreased a respective 55, 88, and 297 N; the medial contact forces decreased at most a respective 90, 190, and 570 N; external tibial rotation in early flexion increased by a respective 4.6°, 1.1°, and 3.3°; and varus angulation of the tibia relative to the femur in late flexion increased by 8.4°, 8.0°, and 7.9°, respectively. With 3° of femoral component external rotation relative to the posterior condylar axis, the femoral component was still externally rotated by up to 2.7° relative to the sTEA in these three neutrally aligned knees. Variations in MCL force from knee to knee with 3° of femoral component external rotation were related to the ratio of the distances from the femoral insertion of the MCL to the posterior and distal cuts of the implant; the closer this ratio was to 1, the more uniform were the MCL tensions from 0° to 90° flexion. Conclusions A larger ratio of distances from the femoral insertion of the MCL to the posterior and distal cuts may cause clinically relevant increases in both MCL tension and compartmental contact forces. Clinical Relevance To obtain more consistent ligament tensions through flexion, it may be important to locate the posterior and distal aspects of the femoral component with respect to the proximal insertion of the MCL such that a ratio of 1 is achieved.

Musculoskeletal Model During Treadmill Gait

Detailed knowledge of joint kinematics and loading is essential for improving the design and surgical outcomes of total knee replacements as well as tissue engineering applications. Dynamic loading is a contributing factor in the development of joint osteoarthritis and in total knee replacement wear. Dynamic computational models in which muscle, ligament, and joint loads are predicted concurrently would be ideal clinical tools for surgery planning and for implant design. An important obstacle in clinical applications of computational models is validation of the estimated in-vivo loads.Copyright

Tibio-Femoral Contact Pressure During Gait

Detailed analysis of knee joint loading during ambulatory activities is of utmost importance for improving the design of total knee replacement components and the outcome of the surgical procedures. A dynamic computational model capable of concurrent predictions of muscle, ligament, and articular surface contact forces would be the ideal tool for enhancing our knowledge of these in-vivo loads and for exploring different loading scenarios.Copyright

Musculoskeletal Model of the Human Knee With Representation of Menisci During the Stance Phase of a Walk Cycle

Movement simulation and musculoskeletal modeling can predict muscle forces, but current methods are hindered by simplified representations of joint structures. Simulations that incorporate muscle forces, an anatomical representation of the natural knee, and contact mechanics would be a powerful tool in orthopedics. This study combined a validated anatomical model of a knee joint with menisci and a musculoskeletal model of the human lower extremity. A forward-dynamics muscle driven simulation of the stance phase of a walk cycle was simulated in LifeMOD (Lifemodeler, Inc) and muscle forces and ground reaction forces were estimated. The predicted forces were evaluated using test data provided by Vaughan CL. et al. (1999).© 2012 ASME