Fluoroscopy-based tracking of femoral kinematics with statistical shape models.
- Valenti M Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Colombo 40, 20133, Milan, Italy. marta.valenti@polimi.it.
- De Momi E Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Colombo 40, 20133, Milan, Italy.
- Yu W Universität Bern, Staffaucherstr. 78, 3014, Bern, Switzerland.
- Ferrigno G Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Colombo 40, 20133, Milan, Italy.
- Akbari Shandiz M University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
- Anglin C University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
- Zheng G Universität Bern, Staffaucherstr. 78, 3014, Bern, Switzerland.
- 2015-09-28
Published in:
- International journal of computer assisted radiology and surgery. - 2016
Computer-assisted surgery
Digitally reconstructed radiographs
Image processing
Statistical shape models
Algorithms
Biomechanical Phenomena
Female
Femur
Fluoroscopy
Humans
Image Processing, Computer-Assisted
Joint Prosthesis
Knee Joint
Models, Statistical
Osteoarthritis, Knee
Prosthesis Design
Range of Motion, Articular
Tomography, X-Ray Computed
English
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).
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).
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1007/s11548-015-1299-6
- PMID 26410843
- Persistent URL
- https://sonar.ch/global/documents/278622
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