X-ray coronary angiography background subtraction by adaptive weighted total variation regularized online RPCA.

Abstract

Coronary X-ray angiograms (XCA) are widely used in diagnosing and treating cardiovascular diseases. Various structures with independent motion patterns in the background of XCA images and limitations in the dose of injected contrast agent have resulted in low-contrast XCA images. Background subtraction methods have been developed to enhance the visibility and contrast of coronary vessels in XCA sequences, consequently reducing the requirement for excessive contrast agent injections. The current study proposes an adaptive weighted total variation regularized online RPCA (WTV-ORPCA) method, which is a low-rank and sparse subspaces decomposition approach to subtract the background of XCA sequences. In the proposed method, the images undergo initial preprocessing using morphological operators to eliminate large-scale background structures and achieve image homogenization. Subsequently, the decomposition algorithm decomposes the preprocessed images into background and foreground subspaces. This step applies an adaptive weighted total variation (TV) constraint to the foreground subspace to ensure the spatial coherency of the finally extracted coronary vessel images. To evaluate the effectiveness of the proposed background subtraction method, some qualitative and quantitative experiments are conducted on two clinical and synthetic low-contrast XCA datasets containing videos from 21 patients. The obtained results are compared with six state-of-the-art methods employing three different assessment criteria. By applying the proposed method to the clinical dataset, the mean values of the global contrast-to-noise ratio (GCNR), local contrast-to-noise ratio (LCNR), structural similarity index (SSIM), and reconstruction error (RE) are obtained as 5.976, 3.173, 0.987, and 0.026, respectively. These criteria over the low-contrast synthetic dataset were 4.851, 2.942, 0.958, and 0.034, respectively. The findings demonstrate the superiority of the proposed method in improving the contrast and visibility of coronary vessels, preserving the integrity of the vessel structure, and minimizing reconstruction errors without imposing excessive computational complexity.&#xD.