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Deep learning has been successfully applied to low-dose CT (LDCT) imagedenoising for reducing potential radiation risk. However, the widely reportedsupervised LDCT denoising networks require a training set of paired images,which is expensive to obtain and cannot be perfectly simulated. Unsupervisedlearning utilizes unpaired data and is highly desirable for LDCT denoising. Asan example, an artifact disentanglement network (ADN) relies on unparied imagesand obviates the need for supervision but the results of artifact reduction arenot as good as those through supervised learning.An important observation isthat there is often hidden similarity among unpaired data that can be utilized.This paper introduces a new learning mode, called quasi-supervised learning, toempower the ADN for LDCT image denoising.For every LDCT image, the best matchedimage is first found from an unpaired normal-dose CT (NDCT) dataset. Then, thematched pairs and the corresponding matching degree as prior information areused to construct and train our ADN-type network for LDCT denoising.Theproposed method is different from (but compatible with) supervised andsemi-supervised learning modes and can be easily implemented by modifyingexisting networks. The experimental results show that the method is competitivewith state-of-the-art methods in terms of noise suppression and contextualfidelity. The code and working dataset are publicly available athttps://github.com/ruanyuhui/ADN-QSDL.git.

QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data