Abstract：Coronary angiography records the dynamic process of developing vessels with blood flow, which is interfered by cardiac motion, resulting in poor image quality, seriously affecting the diagnosis of physicians, and at the same time is not conducive to the intelligent assisted diagnosis of coronary heart disease. In this paper, a vessel enhancement network for coronary angiography with multi-scale attention based on CNN was proposed. The network consisted of multi-scale attention block (MAB) and large kernel attention tail (LKAT). The MAB contained multi-scale large kernel attention (MLKA) and gated spatial attention block (GSAB), the module was able to extract more local and global vessel information while avoiding the grid effect. The LKAT showed the ability to aggregate long-range information, which improved the characterization of reconstructed vascular features and thus enhanced the reconstruction quality of coronary angiography images. The 2 666 coronary datasets in the experiment were manually labeled by medical expert pairs, and the obtained vessel segmentation labels were used as masks, which were superimposed onto the Gaussian-filtered preprocessed images as coronary enhancement labels. Compared with the existing state-of-the-art methods, the enhancement effect was remarkable, with PSNR and SSIM reaching 34.880 1 and 0.973 2, respectively. Moreover, the enhanced segmentation results achieved Dice and IoU of 0.851 4 and 0.741 3, respectively, with Acc and Recall reaching 98.55% and 89.05%. The experimental results showed that the method realizes intelligent enhancement of coronary angiography images, and it also facilitates the follow-up of the intelligent auxiliary diagnosis of coronary heart disease processing.
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