Medical imaging denoising faces great challenges, yet is in great demand. With its distinctive characteristics, medical imaging denoising in the image domain requires innovative deep learning strategies. In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily. A medical image denoising pipeline based on the CNCL strategy is presented, and is implemented as a generative adversarial network, where various representative networks (including U-Net, DnCNN, and SRDenseNet) are investigated as the predictors. The performance of these implemented models has been validated on medical imaging datasets including CT, MR, and PET. The results show that this strategy outperforms state-of-the-art denoising algorithms in terms of visual quality and quantitative metrics, and the strategy demonstrates a robust generalization capability. These findings validate that this simple yet effective strategy demonstrates promising potential for medical image denoising tasks, which could exert a clinical impact in the future. Code is available at: https://github.com/gengmufeng/CNCL-denoising.

中文翻译：

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医学图像去噪的内容-噪声互补学习


医学成像去噪面临着巨大的挑战，但需求也很大。图像领域的医学成像去噪具有其鲜明的特点，需要创新的深度学习策略。在本研究中，我们提出了一种简单而有效的策略，即内容噪声互补学习（CNCL）策略，其中使用两个深度学习预测器来互补地学习图像数据集各自的内容和噪声。提出了一种基于 CNCL 策略的医学图像去噪流程，并将其实现为生成对抗网络，其中各种代表性网络（包括 U-Net、DnCNN 和 SRDenseNet）作为预测器进行研究。这些实施模型的性能已在包括 CT、MR 和 PET 在内的医学成像数据集上得到验证。结果表明，该策略在视觉质量和定量指标方面优于最先进的去噪算法，并且该策略表现出强大的泛化能力。这些发现证实，这种简单而有效的策略在医学图像去噪任务中展现出巨大的潜力，可能在未来产生临床影响。代码位于：https://github.com/gengmufeng/CNCL-denoising。