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Predicting 3D pose in partially overlapped X-ray images of knee prostheses using model-based Roentgen stereophotogrammetric analysis (RSA)

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Abstract

After total knee replacement, the model-based Roentgen stereophotogrammetric analysis (RSA) technique has been used to monitor the status of prosthetic wear, misalignment, and even failure. However, the overlap of the prosthetic outlines inevitably increases errors in the estimation of prosthetic poses due to the limited amount of available outlines. In the literature, quite a few studies have investigated the problems induced by the overlapped outlines, and manual adjustment is still the mainstream. This study proposes two methods to automate the image processing of overlapped outlines prior to the pose registration of prosthetic models. The outline-separated method defines the intersected points and segments the overlapped outlines. The feature-recognized method uses the point and line features of the remaining outlines to initiate registration. Overlap percentage is defined as the ratio of overlapped to non-overlapped outlines. The simulated images with five overlapping percentages are used to evaluate the robustness and accuracy of the proposed methods. Compared with non-overlapped images, overlapped images reduce the number of outlines available for model-based RSA calculation. The maximum and root mean square errors for a prosthetic outline are 0.35 and 0.04 mm, respectively. The mean translation and rotation errors are 0.11 mm and 0.18°, respectively. The errors of the model-based RSA results are increased when the overlap percentage is beyond about 9 %. In conclusion, both outline-separated and feature-recognized methods can be seamlessly integrated to automate the calculation of rough registration. This can significantly increase the clinical practicability of the model-based RSA technique.

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Acknowledgments

The authors are very grateful to the Shin Kong Wu Ho-Su memorial hospital and Taipei Medical University, Taiwan for the funding support (Project No. SKH-TMU-100-14).

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Authors and Affiliations

  1. Institute of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC

    Chi-Pin Hsu & Shang-Chih Lin

  2. Department of Orthopedic Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan, ROC

    Kao-Shang Shih

  3. School of Medicine, Fu Jen Catholic University, Taipei, Taiwan, ROC

    Kao-Shang Shih

  4. Biomechanics Laboratory, Department of Medical Research, Mackay Memorial Hospital, Taipei, Taiwan, ROC

    Chang-Hung Huang

  5. School of Medicine, Taipei Medical University, Taipei, Taiwan, ROC

    Chian-Her Lee

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  1. Chi-Pin Hsu
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  2. Shang-Chih Lin
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  3. Kao-Shang Shih
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  4. Chang-Hung Huang
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  5. Chian-Her Lee
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Corresponding author

Correspondence to Chian-Her Lee.

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Chi-Pin Hsu and Kao-Shang Shih have contributed equally to this work.

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Hsu, CP., Lin, SC., Shih, KS. et al. Predicting 3D pose in partially overlapped X-ray images of knee prostheses using model-based Roentgen stereophotogrammetric analysis (RSA). Med Biol Eng Comput 52, 1061–1071 (2014). https://doi.org/10.1007/s11517-014-1206-2

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  • DOI: https://doi.org/10.1007/s11517-014-1206-2

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