College of Computer Science, Sichuan University, Chengdu, 610041, People's Republic of China.
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, People's Republic of China.
Phys Med Biol. 2022 Apr 11;67(8). doi: 10.1088/1361-6560/ac5f70.
Currently, the field of low-dose CT (LDCT) denoising is dominated by supervised learning based methods, which need perfectly registered pairs of LDCT and its corresponding clean reference image (normal-dose CT). However, training without clean labels is more practically feasible and significant, since it is clinically impossible to acquire a large amount of these paired samples. In this paper, a self-supervised denoising method is proposed for LDCT imaging.The proposed method does not require any clean images. In addition, the perceptual loss is used to achieve data consistency in feature domain during the denoising process. Attention blocks used in decoding phase can help further improve the image quality.In the experiments, we validate the effectiveness of our proposed self-supervised framework and compare our method with several state-of-the-art supervised and unsupervised methods. The results show that our proposed model achieves competitive performance in both qualitative and quantitative aspects to other methods.Our framework can be directly applied to most denoising scenarios without collecting pairs of training data, which is more flexible for real clinical scenario.
目前，基于监督学习的方法主导着低剂量 CT（LDCT）去噪领域，这些方法需要 LDCT 和其对应的干净参考图像（标准剂量 CT）完全配准的对。然而，在没有干净标签的情况下进行训练更具有实际意义，因为在临床上不可能获取大量的这些配对样本。本文提出了一种用于 LDCT 成像的自监督去噪方法。所提出的方法不需要任何干净的图像。此外，在去噪过程中，感知损失用于在特征域中实现数据一致性。解码阶段使用的注意力块可以帮助进一步提高图像质量。在实验中，我们验证了所提出的自监督框架的有效性，并将我们的方法与几种最先进的监督和无监督方法进行了比较。结果表明，我们提出的模型在定性和定量方面都达到了与其他方法相当的性能。我们的框架可以直接应用于大多数去噪场景，而无需收集训练数据对，这对于实际的临床场景更加灵活。
