基于改进U-net的自监督低剂量CT图像去噪算法研究

Research on self-supervised low-dose CT image denoising algorithm based on improved U-net

针对低剂量CT(low-dose CT,LDCT)图像去噪过程中配对数据难以获取的问题,本文提出了一种基于注意力机制和联合损失的自监督LDCT图像去噪算法。在该算法中,利用边缘增强后的U-net网络完成LDCT图像的特征提取,在网络框架中引入通道和像素注意力机制,以提高网络对噪声和伪影的抑制能力。同时使用联合损失避免传统损失对图像造成的图像过平滑问题,使得去噪后图像更加接近原图像。实验结果表明:所提出的算法可有效抑制LDCT图像的噪声,保留图像的纹理细节。经过算法处理后的LDCT图像的峰值信噪比(peak signal-to-noise ratio,PSNR)提高了16.40%,结构相似性(structural similarity,SSIM)提高了9.60%。在无配对数据下,该方法可有效保留细节并减少低剂量扫描产生的噪声,为临床LDCT图像去噪提供新思路。

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.