Self-supervised learning is increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to acquire in clinical practice. In this paper, we propose a novel self-supervised training strategy that relies exclusively on LDCT images. We introduce a step-wise blind-spot denoising mechanism that enforces conditional independence in a progressive manner, enabling more fine-grained denoising learning. In addition, we add Gaussian noise to LDCT images, which acts as a regularization and mitigates overfitting. Extensive experiments on the Mayo LDCT dataset demonstrate that the proposed method consistently outperforms existing self-supervised approaches and achieves performance comparable to, or better than, several representative supervised denoising methods.

翻译：自监督学习在低剂量计算机断层扫描（LDCT）图像去噪领域日益受到关注，因为它减少了对配对正常剂量CT（NDCT）数据的依赖，而这类数据在临床实践中往往难以获取。本文提出了一种仅依赖LDCT图像的新型自监督训练策略。我们引入了一种逐步盲点去噪机制，以渐进方式强制条件独立性，从而实现更细粒度的去噪学习。此外，我们在LDCT图像中添加高斯噪声，作为正则化手段以减轻过拟合。在梅奥LDCT数据集上的大量实验表明，所提方法持续优于现有的自监督方法，并取得了与多种代表性监督去噪方法相当或更优的性能。