Motion measurements in low-contrast X-ray imagery
Measuring motion in medical imagery becomes more and more important, in particular for object tracking, image registration, and local displacement measurements. Such measurements are especially difficult in megavoltage X-ray images (portal images), which are used to control the position of patients in high precision radiotherapy. Low contrast, blur, and noise render accurate measurements difficult.
In this work we review the framework of a generic matching algorithm only based on the image signal and not on binary image features. Thus, the often unreliable step of feature extraction in such imagery is circumvented. Another major advantage is the possibility of self-diagnosis, which is used for restricting the transformation in motion measurements if the image quality is not sufficient.
The method of digitally reconstructed radiographs (DRR) allow for the computation of error free reference images, avoiding the additional step of therapy simulation. The multi-modal match between such DRRs and portal images lead to an estimate of the patient position during radiotherapy treatment. Results of generated data with known ground truth as well as results of a multi-modal match are presented.
KeywordsPortal Image Electronic Portal Imaging Device Template Region Cofactor Matrix Multiple Template
- [Berger and Danuser 1997]Martin Berger and Gaudenz Danuser. Deformable multi template matching with application to portal images. In Proc. CVPR ’97, pages 374–379. IEEE Computer Society Press, 1997.Google Scholar
- [Berger 1998]Martin Berger. The framework of least squares template matching. Technical Report 180, Image Science Lab, ETH Zürich, 1998. Available at http://www.vision.ee.ethz.ch/.Google Scholar
- [Danuser and Mazza 1996]G. Danuser and E. Mazza. Observing deformations of 20 nanometer with a low numerical aperture light microscope. In Optical Inspection and Micromeasurements, volume 2782, pages 180–191. SPIE, 1996.Google Scholar
- [Dong and Boyer 1996]
- [Förstner 1987]
- [Fritsch et al. 1995]
- [Gilhuijs and van Herk 1993]
- [Grün 1985]A. Grün. Adaptive least squares correlation: A powerful image matching technique. South African J. of Photogrammetry, 14(3):175–187, 1985.Google Scholar
- [Koch 1988]K. R. Koch. Parameter estimation and hypothesis testing in linear models. Springer, 1988.Google Scholar
- [Lucas and Kanade 1981]Bruce D. Lucas and Takeo Kanade. An iterative image registration technique with an application to stereo vision. In International joint conference on artificial intelligence, pages 674–679, 1981.Google Scholar
- [Moseley and Munro 1994]
- [Unser et al. 1995]M. Unser, P. Thévenaz, L. Chulhee, and U. Ruttimann. Registration and statistical analysis of pet images using the wavelet transform. IEEE Engineering in Medicine and Biology, Sep./Oct. 1995.Google Scholar
- [van Herk and Meertens 1988]