Prediction of the Repair Surface over Cartilage Defects: A Comparison of Three Methods in a Sheep Model

Authors:
Manuela Kunz;Steven Devlin;Ren Hui Gong;Jiro Inoue;Stephen D. Waldman;Mark Hurtig;Purang Abolmaesumi;James Stewart
Affiliations:
School of Computing, Department of Materials and Mechanical Engineering, Department of Surgery, Queen's University, Kingston, Canada;School of Computing, Department of Materials and Mechanical Engineering, Department of Surgery, Queen's University, Kingston, Canada;School of Computing, Department of Materials and Mechanical Engineering, Department of Surgery, Queen's University, Kingston, Canada;School of Computing, Department of Materials and Mechanical Engineering, Department of Surgery, Queen's University, Kingston, Canada;School of Computing, Department of Materials and Mechanical Engineering, Department of Surgery, Queen's University, Kingston, Canada;Ontario Veterinary College, University of Guelph, Guelph, Canada;School of Computing, Department of Materials and Mechanical Engineering, Department of Surgery, Queen's University, Kingston, Canada;School of Computing, Department of Materials and Mechanical Engineering, Department of Surgery, Queen's University, Kingston, Canada
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

Defects in articular cartilage can be repaired through osteochondral transplantation (mosaic arthroplasty), where osteochondral plugs from non-weight-bearing areas of the joint are transferred to the defect site. Incongruity between the plug surface and the adjacent cartilage results in increased contact pressures and poorer outcomes. We compare three methods to predict the desired repair surface for use in computer-assisted mosaic arthroplasty: manual estimation, a cubic spline surface, and a statistical shape atlas of the knee. The cubic spline was found to most accurately match the pre-impact cartilage surface; the atlas was found to match least accurately.