Abstract

Objective: Digital subtraction angiography (DSA) is significantly important for cerebrovascular disease diagnosis and treatment. However, artifacts and noise are inevitable and reduce image quality. These problems could make clinical diagnosis difficult. In this paper, we introduce a novel deep learning architecture, exploiting the information decoupling training strategy to generate highquality DSA images. Methods: We propose the generative decoupling network, a feature decoupling convolutional network, which maximizes the difference between different structures throughout a decoupling training strategy. In this network, an axial residual block and a learnable sampling method are proposed to enhance the strength of feature extraction. Results: The results showed that our proposed method significantly outperforms the existing methods in the DSA generation task. Furthermore, we quantified the method using the metrics of SSIM, PSNR, VSI, FID and FSIM, with the results of 93.57%, 24.18dB, 98.04%, 351.59, and 89.95%, respectively. Conclusion: Our method can produce high-quality DSA images with little or even no artifact and noise. Significance: The proposed method can effectively reduce artifacts and noise, and generate high quality DSA images with complete and clear vascular structures.