In this paper, we introduced a novel deep learning based reconstruction technique using the correlations of all 3 dimensions with each other by taking into account the correlation between 2-dimensional low-dose CT images. Sparse or noisy sinograms are back projected to the image domain with FBP operation, then denoising process is applied with a U-Net like 3 dimensional network called 3D U-NetR. Proposed network is trained with synthetic and real chest CT images, and 2D U-Net is also trained with the same dataset to prove the importance of the 3rd dimension. Proposed network shows better quantitative performance on SSIM and PSNR. More importantly, 3D U-NetR captures medically critical visual details that cannot be visualized by 2D network.

翻译：在本文中,我们引入了一种新的深层次的基于学习的重建技术,利用所有三个维的相互联系,考虑到二维低剂量CT图像之间的相互关系。 粗糙或吵闹的罪恶图被投射到FBP操作的图像域,然后对3维网络(称为3D U-NetR)等U-Net应用去音程序。 提议的网络经过合成和真实胸部CT图像的培训,2D U-Net也经过同样的数据集的培训,以证明第三维的重要性。 拟议的网络显示SSIM和PSNR的更好的定量性能。 更重要的是, 3D U-NetR捕捉了医学上至关重要的直观细节,而2D网络无法将这些细节视视视视视视。