In clinical examinations and diagnoses, low-dose computed tomography (LDCT) is crucial for minimizing health risks compared with normal-dose computed tomography (NDCT). However, reducing the radiation dose compromises the signal-to-noise ratio, leading to degraded quality of CT images. To address this, we analyze LDCT denoising task based on experimental results from the frequency perspective, and then introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. The proposed WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM). First, WIA is introduced to align NDCT with LDCT by mainly adding noise to the high-frequency components, which is the main difference between LDCT and NDCT. Second, to better capture high-frequency components and detailed information, Frequency-Aware Multi-scale Loss (FAM) is proposed by effectively utilizing multi-scale feature space. Extensive experiments on two public LDCT denoising datasets demonstrate that our WIA-LD2ND, only uses NDCT, outperforms existing several state-of-the-art weakly-supervised and self-supervised methods.

翻译：在临床检查与诊断中，低剂量计算机断层扫描（LDCT）相比常规剂量计算机断层扫描（NDCT）在降低健康风险方面至关重要。然而，辐射剂量的降低会损害信噪比，导致CT图像质量下降。为解决此问题，我们基于频域视角的实验结果分析LDCT去噪任务，继而提出一种仅使用NDCT数据的新型自监督CT图像去噪方法WIA-LD2ND。该方法包含两个模块：基于小波的图像对齐模块（WIA）与频率感知多尺度损失函数（FAM）。首先，WIA通过主要向高频分量（即LDCT与NDCT的主要差异）添加噪声实现NDCT与LDCT的对齐；其次，为更好捕获高频分量与细节信息，通过有效利用多尺度特征空间提出频率感知多尺度损失函数（FAM）。在两个公开LDCT去噪数据集上的大量实验表明，仅使用NDCT的WIA-LD2ND方法在性能上优于当前多种先进的弱监督与自监督方法。