Predicting 3D pose in partially overlapped X-ray images of knee prostheses using model-based Roentgen stereophotogrammetric analysis (RSA).
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 po...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 12; pp. 1061 - 1072 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103853465&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103853465 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2014 vid: 52 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103853465 NLM25293422 2012794288 10.1007/s11517-014-1206-2 NLM25293422 103853465 ppf: 1061 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Predicting 3D pose in partially overlapped X-ray images of knee prostheses using model-based Roentgen stereophotogrammetric analysis (RSA). aug: au: Hsu, Chi-Pin Lin, Shang-Chih Shih, Kao-Shang Huang, Chang-Hung Lee, Chian-Her affil: Institute of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC. sug: subj: Equipment Failure Imaging, Three-Dimensional Methods Knee Joint Radiography Joint Prosthesis Radiostereometric Analysis Methods Algorithms Arthroplasty, Replacement, Knee Human Knee Joint Surgery Radiographic Image Interpretation, Computer-Assisted Methods ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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