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Objective: Automatic vascular enhancement in Xray cineangiography is of crucial interest, for instance, for better visualizing and quantifying coronary arteries in diagnostic and interventional procedures. Methods: a novel patch-based adaptive background subtraction method (PABSM) is proposed automatically enhancing vessels in coronary X-ray cineangiography. First, pixels in the cineangiogram is described by the vesselness and Gabor features. Second, a classifier is utilized to separate the cineangiogram into the rough vascular and non-vascular region. Dilation is applied to the classified binary image to include more vascular region. Third, a patch-based background synthesis is utilized to fill the removed vascular region. Results: a database containing 320 cineangiograms of 175 patients was collected, and then an interventional cardiologist annotated all vascular structures. The performance of PABSM is compared with six state-of-the-art vascular enhancement methods regarding the precision-recall curve and Cvalue. The area under the precision-recall curve is, and the C-value is . Conclusion: PABSM can automatically enhance the coronary artery in the cineangiograms. It preserves the integrity of vascular topological structures, particularly in complex vascular regions and removes noise caused by the non-uniform gray level distribution in the cineangiogram. Significance: PABSM can avoid the motion artifacts and eases the subsequent vascular segmentation, which is crucial for the diagnosis and interventional procedures of coronary artery diseases.

In the diagnosis and interventional treatment of coronary artery disease, the 3D time reconstruction of the coronary artery on the basis of X-ray angiographic image sequences can provide dynamic structural information. The synchronization of cardiac phases in the sequences 15 is essential for minimizing the influence of cardiorespiratory motion and realizing precise 3D time reconstruction. Key points are initially extracted from the first image of a sequence. Matching grid points between consecutive images in the sequence are extracted by a multi-layer matching strategy. Then deep motion tracking of key points is achieved by local deformation based on the neighboring grid points of key points. The local deformation is optimized by the Random 20Sample Consensus algorithm. Then, a simple harmonic motion model is utilized to distinguish cardiac motion from other motion sources (e.g. respiratory, patient movement, etc.). Next, the signal which is composed of cardiac motions is filtered by a band-pass filter to reconstruct the cardiac phases. Finally, the synchronization of cardiac phases from different imaging angles is realized by piece-wise linear transformation. The proposed method was evaluated using clinical X-ray angio-25 graphic image sequences from 13 patients. matching points can be accurately computed by the deep motion tracking method. The mean peak temporal distance between the reconstructed cardiac phases and the electrocardiograph signal is s. The correlation between the cardiac phases of the same patient is over . Compared with three other state-of-the-art methods, the proposed method accurately reconstructs and synchronizes the cardiac phases from different sequences of the 30 same patient. The proposed deep motion tracking method is robust and highly effective in synchronizing cardiac phases of angiographic image sequences captured from different imaging angles.

BackgroundAutomatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures.MethodsA novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method.ResultsExperiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence.ConclusionsThe proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

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