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TECHNICAL PAPERS: Joint/Whole Body

An Optimized Image Matching Method for Determining In-Vivo TKA Kinematics with a Dual-Orthogonal Fluoroscopic Imaging System

[+] Author and Article Information
Jeffrey Bingham

Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital/Harvard Medical School and Department of Mechanical Engineering, Massachusetts Institute of Technology, Boston, MA

Guoan Li1

Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital/Harvard Medical School, Boston, MAgli1@partners.org

1

Corresponding author. Bioengineering Laboratory, MGH/Harvard Medical School, 55 Fruit St., GRJ 1215, Boston, MA 02114.

J Biomech Eng 128(4), 588-595 (Jan 06, 2006) (8 pages) doi:10.1115/1.2205865 History: Received June 10, 2005; Revised January 06, 2006

This study presents an optimized matching algorithm for a dual-orthogonal fluoroscopic image system used to determine six degrees-of-freedom total knee arthroplasty (TKA) kinematics in-vivo. The algorithm was evaluated using controlled conditions and standard geometries. Results of the validation demonstrate the algorithm’s robustness and capability of realizing a pose from a variety of initial poses. Under idealized conditions, poses of a TKA system were recreated to within 0.02±0.01 mm and 0.02±0.03 deg for the femoral component and 0.07±0.09 mm and 0.16±0.18 deg for the tibial component. By employing a standardized geometry with spheres, the translational accuracy and repeatability under actual conditions was found to be 0.01±0.06 mm. Application of the optimized matching algorithm to a TKA patient showed that the pose of in-vivo TKA components can be repeatedly located, with standard deviations less than ±0.12 mm and ±0.12 deg for the femoral component and ±0.29 mm and ±0.25 deg for the tibial component. This methodology presents a useful tool that can be readily applied to the investigation of in-vivo motion of TKA kinematics.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 3

Definition of local and global coordinate systems and the transformation of model points

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Figure 4

Outlining procedure. (a) Compartmentalize projected points (b) and (c) determine boundary grids with left-looking outlining technique (d) and (e) select point in each grid that is closest to the outer edge. (f) Completion of algorithm with selected outline points.

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Figure 5

Representation of calculating the minimum distance between projected points and a fluoroscopic outline

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Figure 6

Geometry of standardized test. Spheres two and seven were ceramic, sphere five was tungsten, and the remaining spheres were stainless steel. The spheres were stacked in the vertical plane.

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Figure 1

A dual-orthogonal fluoroscopic system for capturing in-vivo knee joint kinematics

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Figure 2

A virtual dual-orthogonal fluoroscopic system constructed to reproduce the in-vivo knee joint kinematics

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