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Compared with the conventional 1*1 acquisition mode of projection in computedtomography (CT) image reconstruction, the 2*2 acquisition mode improves thecollection efficiency of the projection and reduces the X-ray exposure time.However, the collected projection based on the 2*2 acquisition mode has lowresolution (LR) and the reconstructed image quality is poor, thus limiting theuse of this mode in CT imaging systems. In this study, a novelsinogram-super-resolution generative adversarial network (SSR-GAN) model isproposed to obtain high-resolution (HR) sinograms from LR sinograms, therebyimproving the reconstruction image quality under the 2*2 acquisition mode. Theproposed generator is based on the residual network for LR sinogram featureextraction and super-resolution (SR) sinogram generation. A relativisticdiscriminator is designed to render the network capable of obtaining morerealistic SR sinograms. Moreover, we combine the cycle consistency loss,sinogram domain loss, and reconstruction image domain loss in the total lossfunction to supervise SR sinogram generation. Then, a trained model can beobtained by inputting the paired LR/HR sinograms into the network. Finally, theclassic FBP reconstruction algorithm is used for CT image reconstruction basedon the generated SR sinogram. The qualitative and quantitative results ofevaluations on digital and real data illustrate that the proposed model notonly obtains clean SR sinograms from noisy LR sinograms but also outperformsits counterparts.

Generative Adversarial Network-Based Sinogram Super-Resolution for Computed Tomography Imaging