Marta Valenti1, Elena De Momi2, Weimin Yu3, Giancarlo Ferrigno2, Mohsen Akbari Shandiz4, Carolyn Anglin4, Guoyan Zheng3. 1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Colombo 40, 20133, Milan, Italy. marta.valenti@polimi.it. 2. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Colombo 40, 20133, Milan, Italy. 3. Universität Bern, Staffaucherstr. 78, 3014, Bern, Switzerland. 4. University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
Abstract
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).
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).
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