Spatio-Temporal Constrained Online Layer Separation for Vascular Enhancement in X-Ray Angiographic Image Sequence

Automatic vascular enhancement is crucial to vascular structure identification in X-ray angiographic (XRA) image sequences. In this work, we propose a novel spatio-temporal constrained online layer separation (STOLS) method to achieve vascular enhancement in XRA image sequences. The proposed method integrates the motion consistency of structures into the temporal-constrained online robust principal component analysis (ORPCA) to remove quasi-static structures (e.g., bones) from the enhanced vascular images. Furthermore, smoothing technique is integrated into the spatial-constrained ORPCA to reduce motion artifacts and the noise introduced by non-uniform illumination. To make the proposed method more adaptive to various vascular structures, the spatial-constrained ORPCA is adjusted by an adaptive weight using the proportion of the vessel region in the previous frame. The performance of the proposed method is compared with five state-of-the-art subtraction methods with respect to local and global revised contrast-to-noise ratios (rCNRs) and reconstruction errors. For the proposed method, the local and global rCNRs of the final vessel layer reached 2.54 and 1.24, respectively, while the error between the original and reconstructed images from the respiratory, background, and vessel layer reached 0.0354. The proposed STOLS can enhance the angiograms in a real-time and online manner without fine-tuning parameters, and can thus be used for intra-operation diagnosis and interventional procedures of coronary artery diseases.