Bayesian motion estimation for temporally recursive noise reduction in X-ray fluoroscopy |
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| Affiliation: | 1. Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands;2. Technical University of Delft, Faculty of Civil Engineering and Geosciences, P.O. Box 5048, 2600 GA Delft, The Netherlands;1. Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran;2. Signal and Image Processing Lab. (SIPL), School of Electrical and Computer Eng., Shiraz University, Shiraz, Iran;3. Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX;4. Physics Unit, Department of Radio-oncology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran |
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| Abstract: | This paper develops a Bayesian motion estimation algorithm for motion-compensated temporally recursive filtering of moving low-dose X-ray images (X-ray fluoroscopy). These images often exhibit a very low signal-to-noise ratio. The described motion estimation algorithm is made robust against noise by spatial and temporal regularization. A priori expectations about the spatial and temporal smoothness of the motion vector field are expressed by a generalized Gauss-Markov random field. The advantage of using a generalized Gauss-Markov random field is that, apart from smoothness, it also captures motion edges without requiring an edge detection threshold. The costs of edges are controlled by a single parameter, by means of which the influence of the regularization can be tuned from a median-filter-like behaviour to a linear-filter-like one. |
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