Jessica C. Küpper

Differences in patellofemoral contact mechanics associated with patellofemoral pain syndrome

Patellofemoral pain syndrome (PFPS) is a disorder of the patellofemoral (PF) joint in which abnormal tracking is often cited as a factor in pain development. PF tracking is partially dependent on passive stabilizers (ex: PF geometry). Relations amongst PFPS, PF tracking, and contact mechanics are poorly understood. In-vivo investigation of passive PF joint stabilizers including PF tracking, contact mechanics, cartilage thickness, and patellar shape will allow structural characterization of the PF joint and may highlight differences associated with PFPS. This study examined the role that passive stabilizers play in PFPS (n=10) versus healthy subjects (n=10). PF tracking (contact area centroid migration), cartilage thickness, shape, congruence, and contact patterns were quantified using magnetic resonance imaging during isometric loading at 15 degrees , 30 degrees , and 45 degrees of knee flexion. Distinct relationships were identified between patellar shape and tracking and contact, particularly at low flexion (15-30 degrees ). Healthy subjects exhibited distinct PF tracking and contact patterns related to Type I patella shape (80%) with increasing total contact area (p<0.001) and proximal centroid migration (15-30 degrees p=0.012; 30-45 degrees p<0.001) for increasing knee angles. PFPS subjects deviated from these patterns at low flexion, demonstrating higher total contact area than healthy subjects (p=0.046 at 15 degrees ), lack of proximal centroid migration (15-30 degrees ), and more Type II (30%) and III (20%) patella shapes. This study highlights a new finding that patellar shape combined with low degrees of flexion (15-30 degrees ) may be important to consider, as this is where PFPS tracking and contact patterns deviate from healthy.

Analysis Techniques for Congruence of the Patellofemoral Joint

Quantifying joint congruence may help to understand the relationship between joint function and health. In previous studies, a congruence index (CI) has been used to define subject-specific joint congruence. However, the sensitivity of the CI algorithm to surface representation was unknown. The purpose of this study was to assess the effects of applying five modifications (M1-M5) to the CI algorithm to determine whether the magnitude and variability of the patellofemoral CI is dependent on the surface representation used. The five modifications focused on calculating the CI based on the principal curvature (M1) at the centroid of the contact region, (M2) using an root mean square value for the contact region, (M3) using a mean value for the contact region, (M4) using all digitized points of the patellar surface, and (M5) using all digitized points in contact. The CI found using the contact area (M1, M2, M3, and M5) provides a local measure for congruence, which was shown to increase (decreasing CI) with increasing joint angle. In ten healthy subjects measured with magnetic resonance (MR) images, the patellofemoral joint became significantly more congruent as the knee angle increased from 15 deg to 45 deg using method M5. The magnitude and variability of the patellofemoral CI was dependent on the surface representation used, suggesting that standardization of the surface representation is important to provide a consistent measure. Specifically, M5 provides a local measure of joint congruence, which can account for joint position and orientation. M5 balances the ability to detect differences in congruence between knee angles without introducing high variability.

Error reduction in the finite helical axis for knee kinematics

Abstract Traditionally the FHA is calculated stepwise between data points (sFHA), requiring down sampling to achieve a sufficiently large step size to minimize error. This paper proposes an alternate FHA calculation approach (rFHA), using a unique reference position to reduce error associated with small rotation angles. This study demonstrated error reduction using the rFHA approach relative to the sFHA approach. Furthermore, the rFHA in the femur is defined at each time point providing a continuous representation of joint motion. These characteristics enable the rFHA to quantify small differences in knee joint motion, providing an excellent measure to quantify knee joint stability.

Quantifying in vivo laxity in the anterior cruciate ligament and individual knee joint structures

A custom knee loading apparatus (KLA), when used in conjunction with magnetic resonance imaging, enables in vivo measurement of the gross anterior laxity of the knee joint. A numerical model was applied to the KLA to understand the contribution of the individual joint structures and to estimate the stiffness of the anterior-cruciate ligament (ACL). The model was evaluated with a cadaveric study using an in situ knee loading apparatus and an ElectroForce test system. A constrained optimization solution technique was able to predict the restraining forces within the soft-tissue structures and joint contact. The numerical model presented here allowed in vivo prediction of the material stiffness parameters of the ACL in response to applied anterior loading. Promising results were obtained for in vivo load sharing within the structures. The numerical model overestimated the ACL forces by 27.61–92.71%. This study presents a novel approach to estimate ligament stiffness and provides the basis to develop a robust and accurate measure of in vivo knee joint laxity.

An application of algebraic method on MR T2 imaging of knee articular cartilage

The computation of relaxation time from quantitative magnetic resonance (MR) imaging depends on the applied algorithms. The purpose of this project was to use the algebraic curve fitting algorithm to quantify T2 mapping of knee articular cartilage for T2 relaxation time calculation. The T2 images of a healthy male volunteers right knee tibiofemoral joint cartilage were generated by a 3T MR imaging scanner using a spin echo multislice multiecho (MSME) Carr-Purcell Meiboom-Gill (CPMG) sequence. The medial and lateral condyle cartilage regions were further subdivided into three compartments - anterior, middle and posterior for identifying T2 values variation in between them. The T2 relaxation time mean and standard deviation in each region of interest (ROI) was calculated using the algebraic fitting algorithm and compared with conventional nonlinear algorithms. The results show that the algebraic fitting algorithm is feasible for T2 relaxation time calculation of knee tibiofemoral condyle cartilage. It is not only clear but also sensitive to T2 MR imaging of knee articular cartilage.

Quantification of In Vivo Laxity in the ACL and Individual Knee Joint Structures

In biomechanical terms, passive joint laxity is a measure of joint movement within the constraints of ligaments, capsule, and cartilage [1] when an external force is applied to the joint during a state of muscular relaxation. Excessive knee joint laxity (reduced stiffness) can result from soft tissue injury, such as a ligament tear, or from genetic factors such as benign joint hypermobility syndrome, and can predispose the joint to instability including recurrent dislocations, and low-grade inflammatory arthritis [2]. A novel technique for in vivo measurement of 3D knee joint laxity using magnetic resonance (MR) imaging with a custom knee loading apparatus (KLA) has been developed in our research group [3]. Gross joint laxity is predicted based on joint displacement in response to an applied anterior tibial load. To better understand the link between laxity and instability, and to advance this technique for clinical applications, the laxity of individual joint structures, such as the anterior cruciate ligament (ACL) must be quantified.Copyright

Quantification of In Vivo Knee Joint Laxity

In biomechanical terms, passive joint laxity is a measure of joint movement within the constraints of ligaments, capsule, and cartilage [1] when an external force is applied to the joint during a state of muscular relaxation. Excessive knee joint laxity (reduced stiffness) can result from soft tissue injury, such as a ligament tear, or from genetic factors such as benign joint hypermobility syndrome, and can predispose the joint to instability including recurrent dislocations, and low-grade inflammatory arthritis [2]. The link between laxity and instability may be better understood if laxity can be reliably and accurately quantified. To more fully understand the underlying joint mechanics, it is necessary to quantify both gross knee joint stiffness as well as the stiffness characteristics of individual joint structures, such as the anterior cruciate ligament (ACL).Copyright

Knee Joint Motion Quantified Using the Finite Helical Axis Method

The knee is a complex joint comprised of two main bones (femur and tibia) that articulate in a stable manner through the support of surrounding meniscus, musculature, and ligaments. The anterior cruciate ligament (ACL) is one of the main ligaments connecting the femur to the tibia. The ACL restricts anterior translation of the tibia with respect to the femur and aids in preventing internal and external rotation. The ACL is the most commonly injured ligament in the knee [1] and has been shown to increase the risk of cartilage degeneration leading to osteoarthritis (OA) [2]. The mechanics of the joint are altered following an ACL rupture, but the relations between the resulting joint instability and OA are not well understood.Copyright

A Novel Measure of In-Vivo Knee Joint Laxity

Knee joint laxity can result from soft tissue injury, such as an anterior cruciate ligament (ACL) tear, or genetic factors such as joint hypermobility syndrome (JHS). The degree of a subject’s knee laxity along a continuous spectrum depends on the mechanical properties of the structures, and increased motion that typically follows joint injury. At some threshold along the continuum, instability becomes pathologic and the risk of further joint injury increases.Copyright