  Despite the indispensable role of X-ray computed tomography (CT) in
diagnostic medicine field, the associated ionizing radiation is still a major
concern considering that it may cause genetic and cancerous diseases.
Decreasing the exposure can reduce the dose and hence the radiation-related
risk, but will also induce higher quantum noise. Supervised deep learning can
be used to train a neural network to denoise the low-dose CT (LDCT). However,
its success requires massive pixel-wise paired LDCT and normal-dose CT (NDCT)
images, which are rarely available in real practice. To alleviate this problem,
in this paper, a shift-invariant property based neural network was devised to
learn the inherent pixel correlations and also the noise distribution by only
using the LDCT images, shaping into our probabilistic self-learning framework.
Experimental results demonstrated that the proposed method outperformed the
competitors, producing an enhanced LDCT image that has similar image style as
the routine NDCT which is highly-preferable in clinic practice.
