Automatic model-based roentgen stereophotogrammetric analysis (RSA) of total knee prostheses

Syu, Ci Bin; Lai, Jiing Yih; Chang, Ren Yi; Shih, Kao Shang; Chen, Kuo Jen; Lin, Shu

doi:10.1016/j.jbiomech.2011.09.011

Cited by 5 publications

(9 citation statements)

References 26 publications

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“…Those images are non-distortion and high resolution (0.15 mm/pixel). The calibration of images and registration algorithms are based on previous works [17,18]. In general situation, the follow-up images of TKR have two properties for easy and automatic defined the outline of metal prosthesis.…”

Section: Methodsmentioning

confidence: 99%

“…Two-staged registrations (i.e. rough and fine) are used to predict the 3D prosthetic pose [11,17]. During rough registration, the initial values of two rotation angles and three translations for prosthetic models are automatically determined from the X-ray images to improve the efficiency of execution.…”

Section: Feature Recognition Of Knee Prostheses and Rough Registrationmentioning

confidence: 99%

“…The feature-recognized method uses the circle-and line-based features of the knee prostheses to define the 2D positions of the prostheses. Subsequently, the 2D information is mapped to establish 3D coordinates of the prostheses by coordinate transformation [11,17]. b The schematic diagram to illustrate the relationship between the points P CAD,i and P proj,i…”

Section: Feature Recognition Of Knee Prostheses and Rough Registrationmentioning

confidence: 99%

“…For an X-ray image, two types of prosthetic features can be defined to facilitate rough registration [11,17]. For the mediolateral (ML) view, the lines aa (femur) and bb (tibia) are employed to determine the direction of the coordinate axes v f ML and v t ML , respectively (Fig.…”

Section: Feature Recognition Of Knee Prostheses and Rough Registrationmentioning

confidence: 99%

“…Comparatively, model-based RSA is time-consuming, and the technician must often have experience in manual manipulation to precisely match the projective outlines of the CAD model and the X-ray images of the prostheses [1,8,9]. However, advances in computer-aided design (CAD) and some automatic algorithms have hastened the development of the model-based RSA technique [11,17]. Furthermore, some studies developed kinematic estimation and optimization algorithm of knees to increase accuracy and precision with single-plane fluoroscopy [16,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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

Hsu

Lin

Shih

et al. 2014

Med Biol Eng Comput

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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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Section: Methodsmentioning

confidence: 99%

Section: Feature Recognition Of Knee Prostheses and Rough Registrationmentioning

confidence: 99%

Section: Feature Recognition Of Knee Prostheses and Rough Registrationmentioning

confidence: 99%

Section: Feature Recognition Of Knee Prostheses and Rough Registrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Hsu

Lin

Shih

et al. 2014

Med Biol Eng Comput

Self Cite

View full text Add to dashboard Cite

show abstract

Projective geometric model for automatic determination of X-ray-emitting source of a standard radiographic system

García-Ruesgas

Álvarez-Cuervo

Valderrama-Gual

et al. 2018

Computers in Biology and Medicine

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Pose-induced effects of femur and tibia on accuracy of model-based radiostereometric analysis

Lin

Huang

Hsu

et al. 2017

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Automatic model-based roentgen stereophotogrammetric analysis (RSA) of total knee prostheses

Cited by 5 publications

References 26 publications

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

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

Projective geometric model for automatic determination of X-ray-emitting source of a standard radiographic system

Pose-induced effects of femur and tibia on accuracy of model-based radiostereometric analysis

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