QS-ADN: quasi-supervised artifact disentanglement network for low-dose CT image denoising by local similarity among unpaired data

Abstract

Deep learning has been successfully applied to
low-dose CT (LDCT) image denoising for reducing potential
radiation risk. However, the widely reported supervised LDCT
denoising networks require a training set of paired images,
which is expensive to obtain and cannot be perfectly simulated.
Unsupervised learning utilizes unpaired data and is highly
desirable for LDCT denoising. As an example, an artifact
disentanglement network (ADN) relies on unparied images
and obviates the need for supervision but the results of
artifact reduction are not as good as those through supervised
learning. An important observation is that there is often hidden
similarity among unpaired data that can be utilized. This
paper introduces a new learning mode, called quasi-supervised
learning, to empower the ADN for LDCT image denoising.
For every LDCT image, the best matched image is first found
from an unpaired normal-dose CT (NDCT) dataset. Then,
the matched pairs and the corresponding matching degree as
prior information are used to construct and train our ADNtype network for LDCT denoising. The proposed method is
different from (but compatible with) supervised and semisupervised learning modes and can be easily implemented by
modifying existing networks. The experimental results show
that the method is competitive with state-of-the-art methods
in terms of noise suppression and contextual fidelity. The
code and working dataset are publicly available at https:
//github.com/ruanyuhui/ADN-QSDL.git.