A Robust Probabilistic Model for Motion Layer Separation in X-ray Fluoroscopy
Fluoroscopic images are characterized by a transparent projection of 3-D structures from all depths to 2-D. Differently moving structures, for example due to breathing and heartbeat, can be described approximately using independently moving 2-D layers. Separating the fluoroscopic images into the motion layers is desirable to facilitate interpretation and diagnosis. Given the motion of each layer, it is state of the art to compute the layer separation by minimizing a least-squares objective function. However, due to high noise levels and inaccurate motion estimates, the results are not satisfactory in X-ray images.
In this work, we propose a probabilistic model for motion layer separation. In this model, we analyze various data terms and regularization terms theoretically and experimentally. We show that a robust penalty function is required in the data term to deal with noise and shortcomings of the image formation model. For the regularization term, we propose to enforce smoothness of the layers using bilateral total variation. On synthetic data, the mean squared error between the estimated layers and the ground truth is improved by \(18\,\%\) compared to the state of the art. In addition, we show qualitative improvements on real X-ray data.
KeywordsMean Square Error Digital Subtraction Angiography Motion Estimation Regularization Term Image Gradient
The authors gratefully acknowledge funding by Siemens Healthcare and of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German Research Foundation (DFG) in the framework of the German excellence initiative. The concepts and information presented in this paper are based on research and are not commercially available.
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