Low-Dose Ct Reconstruction With Simultaneous Sinogram And Image Domain Denoising By Deep Neural Network

Reducing the radiation dose is always an important topic in modern computed tomography (CT) imaging. As the dose level reduces, the conventional analytical filtered backprojection (FBP) reconstruction algorithm becomes inefficient in generating satisfactory CT images for clinical applications. To overcome such difficulties, in this study we developed a novel deep neural network (DNN) for low dose CT image reconstruction by exploring the simultaneous sinogram domain and CT image domain denoising capabilities. The key idea is to jointly denoise the acquired sinogram and the reconstructed CT image, while reconstructing CT image in an end-to-end manner with the help of DNN. Specifically, this new DNN contains three compartments: the sinogram domain denoising compartment, the sinogram to CT image reconstruction compartment, and the CT image domain denoising compartment. This novel sinogram and image domain based CT image reconstruction network is named as ADAPTIVE-NET. By design, the first and third compartments of ADAPTIVE-NET can mutually update their parameters for CT image denoising during network training. Clearly, one advantage of using ADAPTIVE-NET is that the unique information stored in sinogram can be accessed directly during network training. Validation results obtained from numerical simulations demonstrate that this newly proposed ADAPTIVE-NET can effectively improve the quality of CT images acquired with low radiation dose levels.