Deep learning is a very promising technique for low-dose computed tomography
(LDCT) image denoising. However, traditional deep learning methods require
paired noisy and clean datasets, which are often difficult to obtain. This
paper proposes a new method for performing LDCT image denoising with only LDCT
data, which means that normal-dose CT (NDCT) is not needed. We adopt a
combination including the self-supervised noise2noise model and the
noisy-as-clean strategy. First, we add a second yet similar type of noise to
LDCT images multiple times. Note that we use LDCT images based on the
noisy-as-clean strategy for corruption instead of NDCT images. Then, the
noise2noise model is executed with only the secondary corrupted images for
training. We select a modular U-Net structure from several candidates with
shared parameters to perform the task, which increases the receptive field
without increasing the parameter size. The experimental results obtained on the
Mayo LDCT dataset show the effectiveness of the proposed method compared with
that of state-of-the-art deep learning methods. The developed code is available
at this https URL

这篇论文提出了一种新的方法，用于仅利用低剂量 CT（LDCT）数据进行 LDCT 图像去噪，而无需正常剂量 CT（NDCT）。通过将第二类型噪声添加到 LDCT 图像中，使用自监督的 noise2noise 模型和噪声即洁净策略进行训练，从而实现了提高接收场的模块化 U-Net 结构的选择。通过与最先进的深度学习方法相比，实验结果表明了所提方法的有效性。