  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 ADN-type network for LDCT denoising.The
proposed method is different from (but compatible with) supervised and
semi-supervised 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.
