  Recently, Self-supervised learning methods able to perform image denoising
without ground truth labels have been proposed. These methods create
low-quality images by adding random or Gaussian noise to images and then train
a model for denoising. Ideally, it would be beneficial if one can generate
high-quality CT images with only a few training samples via self-supervision.
However, the performance of CT denoising is generally limited due to the
complexity of CT noise. To address this problem, we propose a novel
self-supervised learning-based CT denoising method. In particular, we train
pre-train CT denoising and noise models that can predict CT noise from Low-dose
CT (LDCT) using available LDCT and Normal-dose CT (NDCT) pairs. For a given
test LDCT, we generate Pseudo-LDCT and NDCT pairs using the pre-trained
denoising and noise models and then update the parameters of the denoising
model using these pairs to remove noise in the test LDCT. To make realistic
Pseudo LDCT, we train multiple noise models from individual images and generate
the noise using the ensemble of noise models. We evaluate our method on the
2016 AAPM Low-Dose CT Grand Challenge dataset. The proposed ensemble noise
model can generate realistic CT noise, and thus our method significantly
improves the denoising performance existing denoising models trained by
supervised- and self-supervised learning.
