Simultaneous denoising and spatial resolution enhancement using convolutional neural network-based linear model in diagnostic CT images

According to an increased use of computed tomography (CT) in medicine, the risk caused by radiation exposure has been considered as one of the major issues. In order to reduce the risk, low-dose CT imaging has attracted attention. However, the low-dose CT imaging causes low spatial resolution (LR) and high noise in reconstructed images. Recently, deep learning-based models have shown a feasibility for reducing noise and improving spatial resolution. However, these models have the drawbacks such as complex structures, large sample size and computational costs. In this study, a simple denoising and super-resolution convolutional neural network (SDSRCNN) was proposed to overcome the limitations of conventional methods. Two networks were trained for the denoising and super-resolution imaging separately, and the trained networks were linearly combined as a single network with a simple architecture. In comparison with conventional methods, denoise-autoencoder (DAE) and super-resolution convolutional neural network (SRCNN) were also implemented. We evaluated the performance of the SDSRCNN in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The results showed that the proposed model could efficiently reduce noise and preserve spatial resolution information comparing the conventional methods. Therefore, the proposed model has the potential for improving the quality of CT images and rejecting the complexity of the conventional methods.