LAUSR

Motion compensation can eliminate the inconsistency of respiratory and cardiac phases during image acquisitions for precise vascular reconstruction in the clinical diagnosis of vascular disease from X-ray angiographic image sequences. In X-ray-based vascular interventional therapy, motion modeling can simulate the process of organ deformation driven by motion signals to display a dynamic organ on angiograms without contrast agent injection. Automatic respiratory signal estimation from X-ray angiographic image sequences is essential for motion compensation and modeling. The effects of respiratory motion, cardiac impulse, and tremor on the structures in the chest and abdomen bring difficulty in extracting accurate respiratory signals individually. In this study, an end-to-end deep learning framework based on motion-flow-guided recurrent network is proposed to address the aforementioned problem. The proposed method utilizes a convolutional neural network to learn the spatial feature of every single frame, and a recurrent neural network to learn the temporal feature of the entire sequence. The combination of the two networks can effectively analyze the image sequence to realize respiratory signal estimation. In addition, the motion-flow between consecutive frames is introduced to provide a dynamic constraint of spatial features, which enables the recurrent network to learn better temporal features from the dynamic spatial features than the static spatial features. We demonstrate the advantages of our approach on the designed datasets, which contain coronary and hepatic angiographic sequences with diaphragm structure, and coronary angiographic sequences without diaphragm structure. Our method improved over the state-of-the-art manifold learning-based methods by 85.7%, 81.5% and 75.3% in respiratory signal accuracy metric on these datasets. The results demonstrate that the proposed method can effectively estimate respiratory signals from multiple motion patterns.