A deep convolutional neural network for simultaneous denoising and deblurring in computed tomography

Computed tomography (CT) has been commonly used for providing medical images. However, CT scans have a potential risk of side-effect due to high radiation dose. In order to overcome this limitation, low-dose CT imaging techniques have been developed by using novel scanners, reconstruction algorithms and adjusted scanning protocols. In spite of these strategies, the conventional low-dose CT imaging techniques are hard to compromise between radiation dose and image quality, leading to noise, blur and artifacts in CT images. These drawbacks of the conventional techniques degrade diagnostic accuracy. In this study, we proposed a deep convolutional neural network (CNN)-based low-dose CT imaging technique for simultaneously reducing noise and blur at once. The proposed method, called denoising and deblurring CNN (DnDbCNN), consisted of two networks, and each network was separately trained for reducing noise and blur in CT images. For combining the trained networks, a convolutional layer was added between the two trained networks, and the combined network was re-trained to update the weights of each layer. Denoising convolutional neural network (DnCNN) and super-resolution convolutional neural network (SRCNN) were also implemented for comparison. We evaluated the performance of the DnDbCNN in terms of mean-square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), coefficient-of-variation (COV) and modulation transfer function (MTF). The results showed that the DnDbCNN improved the noise property, spatial resolution and quantitative accuracy of CT images, and the performance of the DnDbCNN was superior to the conventional methods. As a result, the DnDbCNN can simultaneously reduce noise and blur by combining two networks through three training steps. Therefore, the DnDbCNN has a potential for improving CT image quality with low radiation dose and enables precise diagnosis.