Research Papers

Subject-Specific Analysis of Joint Contact Mechanics: Application to the Study of Osteoarthritis and Surgical Planning

[+] Author and Article Information
Corinne R. Henak

Department of Bioengineering,
University of Utah,
Salt Lake City, UT 84112;
Scientific Computing and Imaging Institute,
University of Utah,
Salt Lake City, UT 84112

Andrew E. Anderson

Department of Bioengineering,
University of Utah,
Salt Lake City, UT;
Scientific Computing and Imaging Institute,
University of Utah,
Salt Lake City, UT;
Department of Orthopaedics,
University of Utah,
Salt Lake City, UT 84108;
Department of Physical Therapy,
University of Utah,
Salt Lake City, UT 84108

Jeffrey A. Weiss

Department of Bioengineering,
University of Utah,
Salt Lake City, UT 84108;
Scientific Computing and Imaging Institute,
University of Utah,
Salt Lake City, UT 84108;
Department of Orthopaedics,
University of Utah,
Salt Lake City, UT 84108
e-mail: jeff.weiss@utah.edu

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received October 17, 2012; final manuscript received January 3, 2013; accepted manuscript posted January 18, 2013; published online February 11, 2013. Editor: Victor H. Barocas.

J Biomech Eng 135(2), 021003 (Feb 11, 2013) (26 pages) Paper No: BIO-12-1492; doi: 10.1115/1.4023386 History: Received October 17, 2012; Revised January 03, 2013; Accepted January 18, 2013

Advances in computational mechanics, constitutive modeling, and techniques for subject-specific modeling have opened the door to patient-specific simulation of the relationships between joint mechanics and osteoarthritis (OA), as well as patient-specific preoperative planning. This article reviews the application of computational biomechanics to the simulation of joint contact mechanics as relevant to the study of OA. This review begins with background regarding OA and the mechanical causes of OA in the context of simulations of joint mechanics. The broad range of technical considerations in creating validated subject-specific whole joint models is discussed. The types of computational models available for the study of joint mechanics are reviewed. The types of constitutive models that are available for articular cartilage are reviewed, with special attention to choosing an appropriate constitutive model for the application at hand. Issues related to model generation are discussed, including acquisition of model geometry from volumetric image data and specific considerations for acquisition of computed tomography and magnetic resonance imaging data. Approaches to model validation are reviewed. The areas of parametric analysis, factorial design, and probabilistic analysis are reviewed in the context of simulations of joint contact mechanics. Following the review of technical considerations, the article details insights that have been obtained from computational models of joint mechanics for normal joints; patient populations; the study of specific aspects of joint mechanics relevant to OA, such as congruency and instability; and preoperative planning. Finally, future directions for research and application are summarized.

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Fig. 1

The effect of congruency and stability on the development of OA in normal and pathologic hips, knees, shoulders, and ankles. Pathologies that make the joints less stable or less congruent tend to increase the incidence of OA. For example, removal of the meniscus in the knee primarily makes the joint less congruent, while removal of the ACL primarily makes the joint less stable.

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Fig. 2

Cartilage structural features, continuum level mechanical behavior, and constitutive models. Left panel—The structure and orientation of collagen and proteoglycan aggregates drive continuum mechanical behavior. Middle panel—Key features of continuum mechanical behavior include tension-compression nonlinearity, anisotropy, viscoelastic material behavior, and swelling. Right panel—Constitutive models capture certain features of cartilage behavior. As a general rule, the simplest constitutive model that captures the behavior of interest should be chosen.

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Fig. 3

High-level overview of methods for generating subject-specific computational models.

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Fig. 4

CT image data from female subjects with dysplastic (left) and normal (right) hip anatomy. Hips with dysplasia have reduced femoral head coverage and poor joint congruency. As a result, when traction is applied, greater separation is obtained between opposing layers of cartilage, thereby yielding more contrast in the joint space.

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Fig. 5

Axial, sagittal, and oblique image acquisition direction in the knee, ankle, hip, and shoulder (lines represent individual slices). For both CT and MR, the chosen scan plane and orientation of the joint influences the degree in which cartilage can be visualized as well as the amount of staircase artifact that will be present in 3D reconstructions. Oblique slices (i.e., 45 deg) are preferred clinically for nonspherical joints, such as the knee and ankle, as they provide optimal visualization of the articulating surface. However, oblique slices may induce a larger degree of staircase artifact, resulting in unrealistic predictions of cartilage mechanics in subsequent contact models. Oblique slices can also be difficult or impossible to obtain and may not yield additional information for spherical joints. Images acquired axially provide worse stair-stepping artifact in the knee and ankle when compared to sagittal or coronal slices.

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Fig. 7

Solute concentration over time in a 2D problem demonstrating the visualization of a scalar result using a fringe plot. The upper cylinder was initially at a uniform solute concentration of 0 mM, and the bottom plate was initially at a uniform solute concentration of 1 mM. The cylinder was displaced into the plate over the first second of analysis and then allowed to relax (based off analysis by Ateshian et al. [96]; solute solubility κ = 1, osmotic coefficient Φ = 1, diffusivity = 5 × 10–4 mm2s–1, free diffusivity = 10–3 mm2s–1, permeability 10–3 mm4N–1s–1, neo-Hookean solid matrix with E = 1 MPa and ν = 0.3, 3-mm radius of upper disk, displaced downward 1.5 mm, analyzed in FEBio).

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Fig. 8

Combination vector and fringe plot of fluid flux in a biphasic analysis (geometry from Ref. [97]). Contact between the two layers forces fluid out radially. The vector plot provides information regarding the direction of fluid flow, which is not clear from the fringe plot alone.

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Fig. 6

Validation of computational prediction of cartilage contact stress via direct comparison with experimental results indicates excellent agreement.

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Fig. 10

Contact pressure patterns in the human hip of ten normal subjects demonstrates large intersubject variability.

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Fig. 9

Maximum shear stress through the thickness as a function of nearly incompressible hyperelastic constitutive model for a plane strain analysis of a cylinder (outer radius of 20 mm, thickness of 2 mm) contacting a plate (thickness of 1 mm). The fringe plot shows results for the spherical fiber distribution model, because minimal differences were visible in the fringe plot between constitutive models. Shear stress was evaluated in the cylindrical layer at the location of peak contact stress (left border of fringe plot). The neo-Hookean constitutive model has both a lower maximum value at the contacting surface and a smaller change in maximum shear stress through the thickness of the layer. The Veronda Westmann and spherical fiber distribution constitutive models both captured larger maximum shear stress below the contacting surface than on the contacting surface. The differences between the neo-Hookean constitutive model and the other two combined with the similarity between the Veronda Westmann and spherical fiber distribution models suggests that material nonlinearity, not fiber reinforcement, is a salient feature for capturing maximum shear stress gradients through the thickness with this simplified geometry.

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Fig. 11

Contact comparisons between a subject-specific FEA model (left) and a subject-specific DEA model (right) indicate good agreement in contact pattern, while the DEA model runs in less than 1% of the time required for the FEA model. This makes DEA an attractive option for analyzing large cohorts (adapted from Ref. [83]).

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Fig. 12

Five key areas for future work for subject-specific computational modeling of joint contact mechanics



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