Development of subject-specific and statistical shape models of the knee using an efficient segmentation and mesh-morphing approach

Subject-specific finite element models developed from imaging data provide functional representation of anatomical structures and have been used to evaluate healthy and pathologic knee mechanics. The creation of subject-specific models is a time-consuming process when considering manual segmentation and hexahedral (hex) meshing of the articular surfaces to ensure accurate contact assessment. Previous studies have emphasized automated mesh mapping to bone geometry from computed tomography (CT) scans, but have not considered cartilage and soft tissue structures. Statistical shape modeling has been proposed as an alternative approach to develop a population of subject models, but still requires manual segmentation and registration of a training set. Accordingly, the aim of the current study was to develop an efficient, integrated mesh-morphing-based segmentation approach to create hex meshes of subject-specific geometries from scan data, to apply the approach to natural femoral, tibial, and patellar cartilage from magnetic resonance (MR) images, and to demonstrate the creation of a statistical shape model of the knee characterizing the modes of variation using principal component analysis. The platform was demonstrated on MR scans from 10 knees and enabled hex mesh generation of the knee articular structures in approximately 1.5h per subject. In a subset of geometries, average root mean square geometric differences were 0.54 mm for all structures and in quasi-static analyses over a range of flexion angles, differences in predicted peak contact pressures were less than 5.3% between the semi-automated and manually generated models. The integrated segmentation, mesh-morphing approach was employed in the efficient development of subject-specific models and a statistical shape model, where populations of subject-specific models have application to implant design evaluation or surgical planning.

A probabilistic approach to quantify the impact of uncertainty propagation in musculoskeletal simulations

Uncertainty that arises from measurement error and parameter estimation can significantly affect the interpretation of musculoskeletal simulations; however, these effects are rarely addressed. The objective of this study was to develop an open-source probabilistic musculoskeletal modeling framework to assess how measurement error and parameter uncertainty propagate through a gait simulation. A baseline gait simulation was performed for a male subject using OpenSim for three stages: inverse kinematics, inverse dynamics, and muscle force prediction. A series of Monte Carlo simulations were performed that considered intrarater variability in marker placement, movement artifacts in each phase of gait, variability in body segment parameters, and variability in muscle parameters calculated from cadaveric investigations. Propagation of uncertainty was performed by also using the output distributions from one stage as input distributions to subsequent stages. Confidence bounds (5-95%) and sensitivity of outputs to model input parameters were calculated throughout the gait cycle. The combined impact of uncertainty resulted in mean bounds that ranged from 2.7° to 6.4° in joint kinematics, 2.7 to 8.1 N m in joint moments, and 35.8 to 130.8 N in muscle forces. The impact of movement artifact was 1.8 times larger than any other propagated source. Sensitivity to specific body segment parameters and muscle parameters were linked to where in the gait cycle they were calculated. We anticipate that through the increased use of probabilistic tools, researchers will better understand the strengths and limitations of their musculoskeletal simulations and more effectively use simulations to evaluate hypotheses and inform clinical decisions.

A review of probabilistic analysis in orthopaedic biomechanics

Probabilistic analysis methods are being increasingly applied in the orthopaedics and biomechanics literature to account for uncertainty and variability in subject geometries, properties of various structures, kinematics and joint loading, as well as uncertainty in implant alignment. As a complement to experiments, finite element modelling, and statistical analysis, probabilistic analysis provides a method of characterizing the potential impact of variability in parameters on performance. This paper presents an overview of probabilistic analysis and a review of biomechanics literature utilizing probabilistic methods in structural reliability, kinematics, joint mechanics, musculoskeletal modelling, and patient-specific representations. The aim of this review paper is to demonstrate the wide range of applications of probabilistic methods and to aid researchers and clinicians in better understanding probabilistic analyses.

Probabilistic finite element prediction of knee wear simulator mechanics

Computational models have recently been developed to replicate experimental conditions present in the Stanmore knee wear simulator. These finite element (FE) models, which provide a virtual platform to evaluate total knee replacement (TKR) mechanics, were validated through comparisons with experimental data for a specific implant. As with any experiment, a small amount of variability is inherently present in component alignment, loading, and environmental conditions, but this variability has not been previously incorporated in the computational models. The objectives of the current research were to assess the impact of experimental variability on predicted TKR mechanics by determining the potential envelope of joint kinematics and contact mechanics present during wear simulator loading, and to evaluate the sensitivity of the joint mechanics to the experimental parameters. In this study, 8 component alignment and 4 experimental parameters were represented as distributions and used with probabilistic methods to assess the response of the system, including interaction effects. The probabilistic FE model evaluated two levels of parameter variability (with standard deviations of component alignment parameters up to 0.5mm and 1 degrees ) and predicted a variability of up to 226% (3.44mm) in resulting anterior-posterior (AP) translation, up to 169% (4.30 degrees ) in internal-external (IE) rotation, but less than 10% (1.66MPa) in peak contact pressure. The critical alignment parameters were the tilt of the tibial insert and the IE rotational alignment of the femoral component. The observed variability in kinematics and, to a lesser extent, contact pressure, has the potential to impact wear observed experimentally.

Finite element-based probabilistic analysis tool for orthopaedic applications

Orthopaedic implants, as well as other physical systems, contain inherent variability in geometry, material properties, component alignment, and loading conditions. While complex, deterministic finite element (FE) models do not account for the potential impact of variability on performance, probabilistic studies have typically predicted behavior from simplified FE models to achieve practical solution times. The objective of this research was to develop an efficient and versatile probabilistic FE tool to quantify the effect of uncertainty in the design variables on the performance of orthopaedic components under relevant conditions. Key aspects of the computational tool developed include parametric and automated FE model creation for changes in dimensional variables, efficient solution using the advanced mean-value (AMV) reliability method, and identification of the most significant design variables. Two orthopaedic applications are presented to demonstrate the ability of the computational tool to efficiently and accurately represent component performance.

Incorporating uncertainty in mechanical properties for finite element-based evaluation of bone mechanics

Finite element (FE) models of bone, developed from computed tomography (CT) scan data, are used to evaluate stresses and strains, load transfer and fixation of implants, and potential for fracture. The experimentally derived relationships used to transform CT scan data in Hounsfield unit to modulus and strength contain substantial scatter. The scatter in these relationships has potential to impact the results and conclusions of bone studies. The objectives of this study were to develop a computationally efficient probabilistic FE-based platform capable of incorporating uncertainty in bone property relationships, and to apply the model to a representative analysis; variability in stresses and fracture risk was predicted in five proximal femurs under stance loading conditions. Based on published variability in strength and modulus relationships derived in the proximal femur, the probabilistic analysis predicted the distributions of stress and risk. For the five femurs analyzed, the 1 and 99 percentile bounds varied by an average of 17.3 MPa for stress and by 0.28 for risk. In each femur, the predicted variability in risk was greater than 50% of the mean risk calculated, with obvious implications for clinical assessment. Results using the advanced mean value (AMV) method required only seven analysis trials (1h) and differed by less than 2% when compared to a 1000-trial Monte-Carlo simulation (400 h). The probabilistic modeling platform developed has broad applicability to bone studies and can be similarly implemented to investigate other loading conditions, structures, sources of uncertainty, or output measures of interest.

Relative contributions of design, alignment, and loading variability in knee replacement mechanics

Substantial variation in total knee replacement (TKR) outcomes exists within the patient population. Some of this variability is due to differences in the design of the implanted components and variation in surgical alignment, while other variability is due to differences in the applied forces and torques due to anatomic and physiological differences within a patient population. We evaluated the relative contributions of implant design, surgical alignment, and patient-specific loading variability to overall tibiofemoral joint mechanics to provide insight into which measures can be influenced through design and surgical decisions, and which are inherently dependent on variation within the patient population and should be considered in the robustness of the implant design and surgical procedure. Design, surgical, and loading parameters were assessed using probabilistic finite element methods during simulated stance-phase gait and squat activities. Patient-specific loading was found to be the primary contributor to joint loading and kinematics during low flexion, particularly under conditions of high external loads (for instance, the gait cycle with high internal-external torque), while design and surgical factors, particularly femoral posterior radius and posterior slope of the tibial insert became increasingly important in TKR performance in deeper flexion.

Effect of variability in anatomical landmark location on knee kinematic description

Small variability associated with identifying and locating anatomical landmarks on the knee has the potential to affect the joint coordinate systems and reported kinematic descriptions. The objectives of this study were to develop an approach to quantify the effect of landmark location variability on both tibiofemoral and patellofemoral kinematics and to identify the critical landmarks and associated degrees of freedom that most affected the kinematic measures. The commonly used three-cylindric open-chain kinematic description utilized measured rigid body kinematics from a cadaveric specimen during simulated gait. A probabilistic analysis was performed with 11 anatomical landmarks to predict the variability in each kinematic. The model predicted the absolute kinematic bounds and offset kinematic bounds, emphasizing profile shape, for each kinematic over the gait cycle, as well as the range of motion. Standard deviations of up to 2 mm were assumed for the anatomical landmark locations and resulted in significant variability in clinically relevant absolute kinematic parameters of up to 6.5 degrees and 4.4 mm for tibiofemoral and 7.6 degrees and 6.5 mm for patellofemoral kinematics. The location of the femoral epicondylar prominences had the greatest effect on both the tibiofemoral and patellofemoral kinematic descriptions. A quantitative understanding of the potential changes in kinematic description caused by anatomical landmark variability is important not only to the accuracy of kinematic gait studies and the evaluation of total knee arthroplasty implant performance, but also may impact component placement decision-making in computer-assisted surgery.

Statistical modeling to characterize relationships between knee anatomy and kinematics

The mechanics of the knee are complex and dependent on the shape of the articular surfaces and their relative alignment. Insight into how anatomy relates to kinematics can establish biomechanical norms, support the diagnosis and treatment of various pathologies (e.g., patellar maltracking) and inform implant design. Prior studies have used correlations to identify anatomical measures related to specific motions. The objective of this study was to describe relationships between knee anatomy and tibiofemoral (TF) and patellofemoral (PF) kinematics using a statistical shape and function modeling approach. A principal component (PC) analysis was performed on a 20-specimen dataset consisting of shape of the bone and cartilage for the femur, tibia and patella derived from imaging and six-degree-of-freedom TF and PF kinematics from cadaveric testing during a simulated squat. The PC modes characterized links between anatomy and kinematics; the first mode captured scaling and shape changes in the condylar radii and their influence on TF anterior-posterior translation, internal-external rotation, and the location of the femoral lowest point. Subsequent modes described relations in patella shape and alta/baja alignment impacting PF kinematics. The complex interactions described with the data-driven statistical approach provide insight into knee mechanics that is useful clinically and in implant design.

Influence of polyethylene creep behavior on wear in total hip arthroplasty

After total hip arthroplasty (THA), polyethylene acetabular liner creep occurs quickly and serves to increases head-liner contact area and decrease contact pressures. What effect these early changes in contact mechanics will have on the wear behavior of the articulation remains unclear, and hence, selection or modification of polyethylene materials for optimal creep and wear performance is impossible. The objective of this study was to determine the influence of polyethylene creep behavior on volumetric wear and linear creep and wear penetration during simulated gait loading conditions. A finite element model of THA articulation was developed, and simultaneous numerical creep and wear simulation was performed to 10 million gait cycles with three levels of polyethylene creep behavior. Long-term volumetric wear and penetration were surprisingly unaffected by the polyethylene creep behavior due to the competing decrease in contact pressures coupled with increased contact area. In addition, variation in contact mechanics with the creep levels studied was only noteworthy in the initial postoperative period; after 1 million gait cycles, peak contact pressures and areas were within 13% regardless of the creep material behavior selected. Femoral head size had considerable impact on wear and penetration, while liner thickness primarily affected only early penetration. These results suggest that polyethylene creep behavior plays a major role in early penetration, but has little influence on the more important long-term volumetric wear.

Probabilistic Modeling of Knee Muscle Moment Arms: Effects of Methods, Origin–Insertion, and Kinematic Variability

In musculoskeletal modeling, reliable estimates of muscle moment arms are an important step in accurately predicting muscle forces and joint moments. The degree of agreement between the two common methods of calculating moment arms-tendon excursion (TE) and geometric origin-insertion, is currently unknown for the muscles crossing the knee joint. Further, measured moment arm data are subject to variability in estimation of attachment sites as points from irregular surfaces on the bones, and due to differences in joint kinematics observed in vivo. Thus, the objectives of the present study were to compare moment arms of major muscles crossing the knee joint obtained from TE and geometric methods using a finite element-based lower extremity model, and to quantify the effects of potential muscle origin-insertion and tibiofemoral kinematic variability on the predicted moment arms using probabilistic methods. A semiconstrained, fixed bearing, posterior cruciate-retaining total knee arthroplasty was included due to available in vivo kinematic data. In this study, muscle origin and insertion locations and kinematic variables were represented as normal distributions with standard deviations of 5 mm for origin-insertion locations and up to 1.6 mm and 3.0 degrees for the kinematic parameters. Agreement between the deterministic moment arm calculations from the two methods was excellent for the flexors, while differences in trends and magnitudes were observed for the extensor muscles. Model-predicted deterministic moment arms from both methods agreed reasonably with the experimental values from available literature. The uncertainty in input parameters resulted in substantial variability in predicted moment arms, with the size of 1-99% confidence interval being up to 41.3 and 35.8 mm for the TE and geometric methods, respectively. The sizeable range of moment arm predictions and associated excursions has the potential to affect a muscle's operating range on the force-length curve, thus affecting joint moments. In this study, moment arm predictions were more dependent on muscle origin-insertion locations than the kinematic variables. The important parameters from the TE method were the origin and insertion locations in the sagittal plane, while the insertion location in the sagittal plane was the dominant parameter using the geometric method.

An efficient probabilistic methodology for incorporating uncertainty in body segment parameters and anatomical landmarks in joint loadings estimated from inverse dynamics

Inverse dynamics is a standard approach for estimating joint loadings in the lower extremity from kinematic and ground reaction data for use in clinical and research gait studies. Variability in estimating body segment parameters and uncertainty in defining anatomical landmarks have the potential to impact predicted joint loading. This study demonstrates the application of efficient probabilistic methods to quantify the effect of uncertainty in these parameters and landmarks on joint loading in an inverse-dynamics model, and identifies the relative importance of the parameters and landmarks to the predicted joint loading. The inverse-dynamics analysis used a benchmark data set of lower-extremity kinematics and ground reaction data during the stance phase of gait to predict the three-dimensional intersegmental forces and moments. The probabilistic analysis predicted the 1-99 percentile ranges of intersegmental forces and moments at the hip, knee, and ankle. Variabilities, in forces and moments of up to 56% and 156% of the mean values were predicted based on coefficients of variation less than 0.20 for the body segment parameters and standard deviations of 2 mm for the anatomical landmarks. Sensitivity factors identified the important parameters for the specific joint and component directions. Anatomical landmarks affected moments to a larger extent than body segment parameters. Additionally, for forces, anatomical landmarks had a larger effect than body segment parameters, with the exception of segment masses, which were important to the proximal-distal joint forces. The probabilistic modeling approach predicted the range of possible joint loading, which has implications in gait studies, clinical assessments, and implant design evaluations.

Effects of knee simulator loading and alignment variability on predicted implant mechanics: a probabilistic study

Inherent variability in total knee arthroplasty loading and alignment, present in vivo and in simulator testing, may ultimately influence polyethylene tibial insert wear and long-term performance. The effect of this variability was quantified on implant kinematics and contact mechanics during simulated gait loading conditions using semi-constrained and unconstrained fixed bearing, cruciate retaining implants. A probabilistic finite element model of the Stanmore knee wear simulator was utilized to estimate the envelope of anterior-posterior (AP) and internal-external (IE) position and contact pressure and to evaluate the variability in corresponding ranges of motion (ROM). Variability levels were represented by standard deviations of up to 10% of the maximum value for load inputs and 0.25 mm and 0.5 degrees for component alignment inputs. Model predictions compared well with experimental simulator results for the semi-constrained implant, with predicted positional envelopes of up to 1.8 mm (AP) an 3.4 degrees (IE) for the semi-constrained and up to 2.6 mm (AP) and 3.7 degrees (IE) for the unconstrained implant at the variability levels evaluated. ROM varied by up to 22%, while peak contact pressure variations averaged less than 2 MPa for both designs. For each implant, loading variability was more influential during the swing phase of gait, while alignment variability affected kinematics more during stance. The relative rank of sensitivities showed differences between the two designs, providing insight into critical parameters affecting kinematics and contact characteristics.

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Overall Strategy

Recent explorations of knee biomechanics have benefited from computational modeling, specifically leveraging advancements in finite element analysis and rigid body dynamics of joint and tissue mechanics. A large number of models have emerged with different levels of fidelity in anatomical and mechanical representation. Adapted modeling and simulation processes vary widely, based on justifiable choices in relation to anticipated use of the model. However, there are situations where modelers' decisions seem to be subjective, arbitrary, and difficult to rationalize. Regardless of the basis, these decisions form the "art" of modeling, which impact the conclusions of simulation-based studies on knee function. These decisions may also hinder the reproducibility of models and simulations, impeding their broader use in areas such as clinical decision making and personalized medicine. This document summarizes an ongoing project that aims to capture the modeling and simulation workflow in its entirety-operation procedures, deviations, models, by-products of modeling, simulation results, and comparative evaluations of case studies and applications. The ultimate goal of the project is to delineate the art of a cohort of knee modeling teams through a publicly accessible, transparent approach and begin to unravel the complex array of factors that may lead to a lack of reproducibility. This manuscript outlines our approach along with progress made so far. Potential implications on reproducibility, on science, engineering, and training of modeling and simulation, on modeling standards, and on regulatory affairs are also noted.

Comparison of patellar bone strain in the natural and implanted knee during simulated deep flexion

Instances of anterior knee pain and patellar fracture are significant complications following total knee replacement (TKR). Bone strain measured in the patella can provide an indication of patellar fracture risk and may also be related to anterior knee pain. The objective of this study was to develop subject-specific finite element models of the patellofemoral (PF) joint including density-mapped material properties to gain insight into the patellar bone strain distribution in the natural and implanted knee. In eight subjects, the volume of bone experiencing strains >0.5% in the implanted condition was ∼200% larger, on average, than the natural condition. An inverse relationship with a correlation of -0.74 was established between postoperative bone volume and strain in the implanted specimens, suggesting that patellar geometry may be a useful indicator of postoperative strain. Comparing strains between regions (superior, inferior, medial, and lateral), it was found that although highly strained bone was evenly distributed between medial and lateral regions in the natural case, the implanted specimens demonstrated significantly larger volumes of highly strained bone medially as a result of substantially lower modulus bone in the medial compartment. Understanding distributions of PF strain may aid in preoperative identification of those patients at risk for patellar fracture or anterior knee pain, guidance regarding altered component placement for at-risk patients, and design of components considering the implications of PF load transfer and patellar strain distribution.

Identifying alignment parameters affecting implanted patellofemoral mechanics

Complications of the patellofemoral (PF) joint remain a common cause for revision of total knee replacements. PF complications, such as patellar maltracking, subluxation, and implant failure, have been linked to femoral and patellar component alignment. In this study, a dynamic finite element model of an implanted PF joint was applied in conjunction with a probabilistic simulation to establish relationships between alignment parameters and PF kinematics, contact mechanics, and internal stresses. Both traditional sensitivity analysis and a coupled probabilistic and principal component analysis approach were applied to characterize relationships between implant alignment and resulting joint mechanics. Critical alignment parameters, and combinations of parameters, affecting PF mechanics were identified for three patellar designs (dome, modified dome, and anatomic). Femoral internal-external (I-E) alignment was identified as a critical alignment factor for all component designs, influencing medial-lateral contact force and anterior-posterior translation. The anatomic design was sensitive to patellar flexion-extension (F-E) alignment, while the dome, as expected, was less influenced by rotational alignment, and more by translational position. The modified dome was sensitive to a combination of superior-inferior, F-E, and I-E alignments. Understanding the relationships and design-specific dependencies between alignment parameters can aid preoperative planning, and help focus instrumentation design on those alignment parameters of primary concern.

The impact of hip implant alignment on muscle and joint loading during dynamic activities

BACKGROUND

Component alignment is an important consideration in total hip arthroplasty. The impact of changes in alignment on muscle forces and joint contact forces during dynamic tasks are not well understood, and have the potential to influence surgical decision making. The objectives of this study were to assess the impact of femoral head/stem and cup component placement on hip muscle and joint contact forces during tasks of daily living and to identify which alignment parameters have the greatest impact on joint loading.

METHODS

Using a series of strength-calibrated, subject-specific musculoskeletal models of patients performing gait, sit-to-stand and step down tasks, component alignments were perturbed and joint contact and muscle forces evaluated.

FINDINGS

Based on the range of alignments reported clinically, variation in head/stem anteversion-retroversion had the largest impact of any degree of freedom throughout all three tasks; average contact forces 413.5 (319.1) N during gait, 262.7 (256.4) N during sit to stand, and 572.7 (228.1) N during the step down task. The sensitivity of contact force to anteversion-retroversion of the head/stem was 31.5 N/° for gait, which was similar in magnitude to anterior-posterior position of the cup (34.6 N/m for gait). Additionally, superior-inferior cup alignment resulted in 16.4 (4.9)° of variation in the direction of the hip joint contact force across the three tasks, with the most inferior cup placements moving the force vector towards the cup equator at the point of peak joint contact force.

INTERPRETATION

A quantitative understanding of the impact and potential tradeoffs when altering component alignment is valuable in supporting surgical decision making.

Investigating gender and ethnicity differences in proximal humeral morphology using a statistical shape model

Morphological variability in the shoulder influences the joint biomechanics and is an important consideration in arthroplasty and implant design. The objectives of this study were to quantify cortical and cancellous proximal humeral morphology and to assess whether shape variation was influenced by gender and ethnicity, with the overarching goal of informing implant design and treatment. A statistical shape model of the proximal humeral cortical and cancellous regions was developed for a training set of 84 subjects of both genders and different ethnicities. Cortical and cancellous bone geometries were reconstructed from CT scans, meshed with triangular elements, and registered to a template. Principal component analysis was applied to quantify modes of variation. Anatomical measurements were computed on the registered geometries to assess correlation with modes of variation. Parallel analysis identified six significant modes of variation, which accounted for 93% of variation in the training set and described scaling (Mode 1), inclination of the head (Modes 2 and 5), and shape of the greater tuberosity and neck region (Modes 3, 4, and 6). Size differences as described by Mode 1 were statistically significant for gender and ethnicity, where female and Asian subjects were smaller than male and Caucasian subjects, respectively; however, differences in other modes were not significant. Cortical thickness of the shaft after normalization by outer diameter was significantly larger for Asian subjects compared to Caucasian subjects. The statistical shape model quantified cortical and cancellous humeral morphology considering gender and ethnicity, providing descriptive data to support surgical planning, and implant design. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:3043-3052, 2018.

Simulated hip abductor strengthening reduces peak joint contact forces in patients with total hip arthroplasty

Lower extremity muscle strength training is a focus of rehabilitation following total hip arthroplasty (THA). Strength of the hip abductor muscle group is a predictor of overall function following THA. The purpose of this study was to investigate the effects of hip abductor strengthening following rehabilitation on joint contact forces (JCFs) in the lower extremity and low back during a high demand step down task. Five THA patients performed lower extremity maximum isometric strength tests and a stair descent task. Patient-specific musculoskeletal models were created in OpenSim and maximum isometric strength parameters were scaled to reproduce measured pre-operative joint torques. A pre-operative forward dynamic simulation of each patient performing the stair descent was constructed using their corresponding patient-specific model to predict JCFs at the ankle, knee, hip, and low back. The hip abductor muscles were strengthened with clinically supported increases (0-30%) above pre-operative values in a probabilistic framework to predict the effects on peak JCFs (99% confidence bounds). Simulated hip abductor strengthening resulted in lower peak JCFs relative to pre-operative for all five patients at the hip (18.9-23.8 ± 16.5%) and knee (20.5-23.8 ± 11.2%). Four of the five patients had reductions at the ankle (7.1-8.5 ± 11.3%) and low back (3.5-7.0 ± 5.3%) with one patient demonstrating no change. The reduction in JCF at the hip joint and at joints other than the hip with hip abductor strengthening demonstrates the dynamic and mechanical interdependencies of the knee, hip and spine that can be targeted in early THA rehabilitation to improve overall patient function.

Statistical shape modeling predicts patellar bone geometry to enable stereo-radiographic kinematic tracking

Complications in the patellofemoral (PF) joint of patients with total knee replacements include patellar subluxation and dislocation, and remain a cause for revision. Kinematic measurements to assess these complications and evaluate implant designs require the accuracy of dynamic stereo-radiographic systems with 3D-2D registration techniques. While tibiofemoral kinematics are typically derived by tracking metallic implants, PF kinematic measurements are difficult as the patellar implant is radiotransparent and a representation of the resected patella bone requires either pre-surgical imaging and precise implant placement or post-surgical imaging. Statistical shape models (SSMs), used to characterize anatomic variation, provide an alternative means to obtain the representation of the resected patella for use in kinematic tracking. Using a virtual platform of a stereo-radiographic system, the objectives of this study were to evaluate the ability of an SSM to predict subject-specific 3D implanted patellar geometries from simulated 2D image profiles, and to formulate an effective data collection methodology for PF kinematics by considering accuracy for a variety of patient pose scenarios. An SSM of the patella was developed for 50 subjects and a leave-one-out approach compared SSM-predicted and actual geometries; average 3D errors were 0.45±0.07mm (mean±standard deviation), which is comparable to the accuracy of traditional segmentation. Further, initial imaging of the patella in five unique stereo radiographic perspectives yielded the most accurate representation. The ability to predict the remaining patellar geometry of the implanted PF joint with radiographic images and SSM, instead of CT, can reduce radiation exposure and streamline in vivo kinematic evaluations.

Effects of rotator cuff tears on muscle moment arms: a computational study

Rotator cuff tears cause morphologic changes to cuff tendons and muscles, which can alter muscle architecture and moment arm. The effects of these alterations on shoulder mechanical performance in terms of muscle force and joint strength are not well understood. The purpose of this study was to develop a three-dimensional explicit finite element model for investigating morphological changes to rotator cuff tendons following cuff tear. The subsequent objectives were to validate the model by comparing model-predicted moment arms to empirical data, and to use the model to investigate the hypothesis that rotator cuff muscle moment arms are reduced when tendons are divided along the force-bearing direction of the tendon. The model was constructed by extracting tendon, cartilage, and bone geometry from the male Visible Human data set. Infraspinatus and teres minor muscle and tendon paths were identified relative to the humerus and scapula. Kinetic and kinematic boundary conditions in the model replicated experimental protocols, which rotated the humerus from 45 degrees internal to 45 degrees external rotation with constant loads on the tendons. External rotation moment arms were calculated for two conditions of the cuff tendons: intact normal and divided tendon. Predicted moment arms were within the 1-99% confidence intervals of experimental data for nearly all joint angles and tendon sub-regions. In agreement with the experimental findings, when compared to the intact condition, predicted moment arms were reduced for the divided tendon condition. The results of this study provide evidence that one potential mechanism for the reduction in strength observed with cuff tear is reduction of muscle moment arms. The model provides a platform for future studies addressing mechanisms responsible for reduced muscle force and joint strength including changes to muscle length-tension operating range due to altered muscle and tendon excursions, and the effects of cuff tear size and location on moment arms and muscle forces.

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Variations in Model Development

The use of computational modeling to investigate knee joint biomechanics has increased exponentially over the last few decades. Developing computational models is a creative process where decisions have to be made, subject to the modelers' knowledge and previous experiences, resulting in the "art" of modeling. The long-term goal of the KneeHub project is to understand the influence of subjective decisions on the final outcomes and the reproducibility of computational knee joint models. In this paper, we report on the model development phase of this project, investigating model development decisions and deviations from initial modeling plans. Five teams developed computational knee joint models from the same dataset, and we compared each teams' initial uncalibrated models and their model development workflows. Variations in the software tools and modeling approaches were found, resulting in differences such as the representation of the anatomical knee joint structures in the model. The teams consistently defined the boundary conditions and used the same anatomical coordinate system convention. However, deviations in the anatomical landmarks used to define the coordinate systems were present, resulting in a large spread in the kinematic outputs of the uncalibrated models. The reported differences and similarities in model development and simulation presented here illustrate the importance of the "art" of modeling and how subjective decision-making can lead to variation in model outputs. All teams deviated from their initial modeling plans, indicating that model development is a flexible process and difficult to plan in advance, even for experienced teams.