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http://dx.doi.org/10.7742/jksr.2020.14.3.211

Image Quality Evaluation in Computed Tomography Using Super-resolution Convolutional Neural Network  

Nam, Kibok (Department of Radiological Science, Konyang University)
Cho, Jeonghyo (Department of Radiological Science, Konyang University)
Lee, Seungwan (Department of Radiological Science, Konyang University)
Kim, Burnyoung (Department of Medical Science, Konyang University)
Yim, Dobin (Department of Medical Science, Konyang University)
Lee, Dahye (Department of Radiological Science, Konyang University)
Publication Information
Journal of the Korean Society of Radiology / v.14, no.3, 2020 , pp. 211-220 More about this Journal
Abstract
High-quality computed tomography (CT) images enable precise lesion detection and accurate diagnosis. A lot of studies have been performed to improve CT image quality while reducing radiation dose. Recently, deep learning-based techniques for improving CT image quality have been developed and show superior performance compared to conventional techniques. In this study, a super-resolution convolutional neural network (SRCNN) model was used to improve the spatial resolution of CT images, and image quality according to the hyperparameters, which determine the performance of the SRCNN model, was evaluated in order to verify the effect of hyperparameters on the SRCNN model. Profile, structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and full-width at half-maximum (FWHM) were measured to evaluate the performance of the SRCNN model. The results showed that the performance of the SRCNN model was improved with an increase of the numbers of epochs and training sets, and the learning rate needed to be optimized for obtaining acceptable image quality. Therefore, the SRCNN model with optimal hyperparameters is able to improve CT image quality.
Keywords
Super-resolution convolutional neural network; Hyperparameter; Computed tomography; Image quality;
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