LOW-DOSE CT DENOISING BASED ON THE SYNERGISTIC NETWORK BETWEEN PERCEPTION LOSS AND ATTENTION MECHANISM
Due to the unique quantum noise,low-dose CT denoising is a difficult task.Most of the deep learning methods for denoising have the problem of mismatch between visual inspection and quantitative indicators in which the quantitative indicators of experimental results are high,but the visual inspection is not well.Therefore,this paper proposes a low-dose CT synergistic denoising network between perceptual loss and attention mechanism.This synergistic mechanism could significantly improve the problem of low quantitative indicators in existing methods.The model also introduced an 8-direction edge detection layer to the beginning of the network,which could extract richer textures and structure information,further improving the network performance.The experimental comparison results based on the phantom data sets and the clinical data sets show that the proposed method has better performance in visual inspection,PSNR and SSIM indicators than the state-of-the-art methods.
国家科技期刊平台
NSTL
万方数据
维普
