Self-supervised Noise2noise Method Utilizing Corrupted Images with a
  Modular Network for LDCT Denoising
Yuting Zhu Qiang He Yudong Yao Yueyang Teng
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 https://github.com/XYuan01/Self-supervised-Noise2Noise-for-LDCT.