In order to solve the difficulty of acquiring paired data in low-dose CT (LDCT) image denoising,a self-supervised LDCT image denoising algorithm based on attention mechanism and compound loss is proposed in this paper.In this algorithm, the feature extraction of LDCT images is completed by using the U-net network after edge enhancement. Channels and pixel attention mechanisms are introduced into the network framework to improve the ability of the network to suppress noise and artifacts.Moreover,in order to make the denoised images closer to the original images,we propose a self-supervised learning scheme with compound loss to avoid the over-smoothing phenomenon caused by the traditional loss.The experimental results show that the proposed algorithm can effectively suppress the noise of LDCT images and recover more texture details in LDCT images.The peak signal-to-noise ratio (PSNR) of the LDCT images processed by the proposed algorithm increased by 16.40% and the structural similarity (SSIM) increased by 9.60%.In the absence of paired data,the proposed method can effectively preserve the details and reduce the noise generated by low-dose scanning,which provides a new idea for clinical LDCT image denoising.