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Research Papers

Subject-Specific Finite Element Modeling of the Tibiofemoral Joint Based on CT, Magnetic Resonance Imaging and Dynamic Stereo-Radiography Data in Vivo

[+] Author and Article Information
Robert E. Carey

Department of Mechanical Engineering
and Materials Science,
Musculoskeletal Modeling Laboratory,
University of Pittsburgh,
3820 South Water Street,
Pittsburgh, PA 15203

Liying Zheng

Department of Orthopaedic Surgery,
Musculoskeletal Modeling Laboratory,
University of Pittsburgh,
3820 South Water Street,
Pittsburgh, PA 15203

Ameet K. Aiyangar

EMPA (Swiss Federal Laboratories
for Materials Science and Research),
Mechanical Systems Engineering (Lab 304),
Ueberlandstrasse 129,
Duebendorf 8400, Switzerland

Christopher D. Harner

Department of Orthopaedic Surgery,
University of Pittsburgh,
UPMC Center for Sports of Medicine,
3200 South Water Street,
Pittsburgh, PA 15203

Xudong Zhang

Department of Orthopaedic Surgery,
Department of Mechanical Engineering and Materials Science;
Department of Bioengineering,
Musculoskeletal Modeling Laboratory,
University of Pittsburgh,
3820 South Water Street,
Pittsburgh, PA 15203
e-mail: xuz9@pitt.edu

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received May 30, 2013; final manuscript received November 18, 2013; accepted manuscript posted December 12, 2013; published online March 24, 2014. Assoc. Editor: Pasquale Vena.

J Biomech Eng 136(4), 041004 (Mar 24, 2014) (8 pages) Paper No: BIO-13-1248; doi: 10.1115/1.4026228 History: Received May 30, 2013; Revised November 18, 2013; Accepted December 12, 2013

In this paper, we present a new methodology for subject-specific finite element modeling of the tibiofemoral joint based on in vivo computed tomography (CT), magnetic resonance imaging (MRI), and dynamic stereo-radiography (DSX) data. We implemented and compared two techniques to incorporate in vivo skeletal kinematics as boundary conditions: one used MRI-measured tibiofemoral kinematics in a nonweight-bearing supine position and allowed five degrees of freedom (excluding flexion-extension) at the joint in response to an axially applied force; the other used DSX-measured tibiofemoral kinematics in a weight-bearing standing position and permitted only axial translation in response to the same force. Verification and comparison of the model predictions employed data from a meniscus transplantation study subject with a meniscectomized and an intact knee. The model-predicted cartilage-cartilage contact areas were examined against “benchmarks” from a novel in situ contact area analysis (ISCAA) in which the intersection volume between nondeformed femoral and tibial cartilage was characterized to determine the contact. The results showed that the DSX-based model predicted contact areas in close alignment with the benchmarks, and outperformed the MRI-based model: the contact centroid predicted by the former was on average 85% closer to the benchmark location. The DSX-based FE model predictions also indicated that the (lateral) meniscectomy increased the contact area in the lateral compartment and increased the maximum contact pressure and maximum compressive stress in both compartments. We discuss the importance of accurate, task-specific skeletal kinematics in subject-specific FE modeling, along with the effects of simplifying assumptions and limitations.

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Figures

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Fig. 1

A flow chart of the FE model development and verification process incorporating multimodality data

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Fig. 2

Experimental setup for measuring 3D TF skeletal kinematics using a dynamic stereo-radiography system

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Fig. 3

FE model geometry development sequence for the tibia

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Fig. 4

Lateral and anterior views of FE models of the meniscectomized knee in (a) MRI-based and (b) DSX-based positions

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Fig. 5

In situ contact area analysis (ISCAA) to determine the contact area, defined as the intersection between femoral and tibial cartilage, by co-registering the MRI-acquired cartilage models with DSX-acquired bone models

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Fig. 6

Left: meniscectomized knee ISCAA results overlapped with (a) MRI-based position and (b) DSX-based position FE model predictions. Right: healthy knee ISCAA results overlapped with (c) MRI-based position and (d) DSX-based position FE model predictions. The green area represents the FE model contact area predictions, while the other colors are the color coded ISCAA estimate. Penetration depth increases from blue to red. M = Medial, L = Lateral, A = Anterior, P = Posterior.

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Fig. 7

Left: contact centroid of ISCAA estimation and (a) MRI-based and (b) DSX-based FE model predictions for left, meniscectomized knee plotted on FE tibial cartilage. Right: contact centroid of ISCAA estimation and (c) MRI-based position and (d) DSX-based position FE model predictions for right, healthy knee plotted on FE tibial cartilage. M = Medial, L = Lateral, A = Anterior, P = Posterior.

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Fig. 8

Average distances between FEM-predicted and ISCAA-estimated contact centroids at different levels of material property variation for both MRI-based and DSX-based models

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