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

Dobin Yim; Burnyoung Kim; Seungwan Lee

doi:10.1117/12.2548378

16 March 2020 Simultaneous denoising and spatial resolution enhancement using convolutional neural network-based linear model in diagnostic CT images

Dobin Yim, Burnyoung Kim, Seungwan Lee

Author Affiliations +

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 1131245 (2020) https://doi.org/10.1117/12.2548378
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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.

Citation Download Citation

Dobin Yim, Burnyoung Kim, and Seungwan Lee "Simultaneous denoising and spatial resolution enhancement using convolutional neural network-based linear model in diagnostic CT images", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131245 (16 March 2020); https://doi.org/10.1117/12.2548378

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KEYWORDS

Computed tomography

Spatial resolution

Denoising

Super resolution

Image resolution

Convolutional neural networks

Signal to noise ratio

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