  This paper proposes a deep learning-based denoising method for noisy low-dose
computerized tomography (CT) images in the absence of paired training data. The
proposed method uses a fidelity-embedded generative adversarial network (GAN)
to learn a denoising function from unpaired training data of low-dose CT (LDCT)
and standard-dose CT (SDCT) images, where the denoising function is the optimal
generator in the GAN framework. This paper analyzes the f-GAN objective to
derive a suitable generator that is optimized by minimizing a weighted sum of
two losses: the Kullback-Leibler divergence between an SDCT data distribution
and a generated distribution, and the $\ell_2$ loss between the LDCT image and
the corresponding generated images (or denoised image). The computed generator
reflects the prior belief about SDCT data distribution through training. We
observed that the proposed method allows the preservation of fine anomalous
features while eliminating noise. The experimental results show that the
proposed deep-learning method with unpaired datasets performs comparably to a
method using paired datasets. A clinical experiment was also performed to show
the validity of the proposed method for noise arising in the low-dose X-ray CT.
