  Ionizing radiation has been the biggest concern in CT imaging. To reduce the
dose level without compromising the image quality, low-dose CT reconstruction
has been offered with the availability of compressed sensing based
reconstruction methods. Recently, data-driven methods got attention with the
rise of deep learning, the availability of high computational power, and big
datasets. Deep learning based methods have also been used in low-dose CT
reconstruction problem in different manners. Usually, the success of these
methods depends on labeled data. However, recent studies showed that training
can be achieved successfully with noisy datasets. In this study, we defined a
training scheme to use low-dose sinograms as their own training targets. We
applied the self-supervision principle in the projection domain where the noise
is element-wise independent which is a requirement for self-supervised training
methods. Using the self-supervised training, the filtering part of the FBP
method and the parameters of a denoiser neural network are optimized. We
demonstrate that our method outperforms both conventional and compressed
sensing based iterative reconstruction methods qualitatively and quantitatively
in the reconstruction of analytic CT phantoms and real-world CT images in
low-dose CT reconstruction task.
