  In this paper, we introduced a novel deep learning-based reconstruction
technique for low-dose CT imaging using 3 dimensional convolutions to include
the sagittal information unlike the existing 2 dimensional networks which
exploits correlation only in transverse plane. In the proposed reconstruction
technique, sparse and noisy sinograms are back-projected to the image domain
with FBP operation, then the denoising process is applied with a U-Net like
3-dimensional network called 3D U-NetR. The proposed network is trained with
synthetic and real chest CT images, and 2D U-Net is also trained with the same
dataset to show the importance of the third dimension in terms of recovering
the fine details. The proposed network shows better quantitative performance on
SSIM and PSNR, especially in the real chest CT data. More importantly, 3D
U-NetR captures medically critical visual details that cannot be visualized by
a 2D network on the reconstruction of real CT images with 1/10 of the normal
dose.
