  Low-dose CT imaging requires reconstruction from noisy indirect measurements
which can be defined as an ill-posed linear inverse problem. In addition to
conventional FBP method in CT imaging, recent compressed sensing based methods
exploit handcrafted priors which are mostly simplistic and hard to determine.
More recently, deep learning (DL) based methods have become popular in medical
imaging field. In CT imaging, DL based methods try to learn a function that
maps low-dose images to normal-dose images. Although the results of these
methods are promising, their success mostly depends on the availability of
high-quality massive datasets. In this study, we proposed a method that does
not require any training data or a learning process. Our method exploits such
an approach that deep convolutional neural networks (CNNs) generate patterns
easier than the noise, therefore randomly initialized generative neural
networks can be suitable priors to be used in regularizing the reconstruction.
In the experiments, the proposed method is implemented with different loss
function variants. Both analytical CT phantoms and real-world CT images are
used with different views. Conventional FBP method, a popular iterative method
(SART), and TV regularized SART are used in the comparisons. We demonstrated
that our method with different loss function variants outperforms the other
methods both qualitatively and quantitatively.
