Fluoroscopy-based tracking of femoral kinematics with statistical shape models

Valenti, Marta; De Momi, Elena; Yu, Weimin; Ferrigno, Giancarlo; Akbari Shandiz, Mohsen; Anglin, Carolyn; Zheng, Guoyan (2016). Fluoroscopy-based tracking of femoral kinematics with statistical shape models. International Journal of Computer Assisted Radiology and Surgery, 11(5), pp. 757-765. Springer 10.1007/s11548-015-1299-6

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PURPOSE: Precise knee kinematics assessment helps to diagnose knee pathologies and to improve the design of customized prosthetic components. The first step in identifying knee kinematics is to assess the femoral motion in the anatomical frame. However, no work has been done on pathological femurs, whose shape can be highly different from healthy ones. METHODS: We propose a new femoral tracking technique based on statistical shape models and two calibrated fluoroscopic images, taken at different flexion-extension angles. The cost function optimization is based on genetic algorithms, to avoid local minima. The proposed approach was evaluated on 3 sets of digitally reconstructed radiographic images of osteoarthritic patients. RESULTS: It is found that using the estimated shape, rather than that calculated from CT, significantly reduces the pose accuracy, but still has reasonably good results (angle errors around 2[Formula: see text], translation around 1.5 mm).

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Yu, Weimin and Zheng, Guoyan

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

1861-6410

Publisher:

Springer

Language:

English

Submitter:

Guoyan Zheng

Date Deposited:

24 May 2017 11:01

Last Modified:

24 May 2017 11:01

Publisher DOI:

10.1007/s11548-015-1299-6

PubMed ID:

26410843

Uncontrolled Keywords:

Computer-assisted surgery; Digitally reconstructed radiographs; Image processing; Statistical shape models

BORIS DOI:

10.7892/boris.82189

URI:

https://boris.unibe.ch/id/eprint/82189

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