• Journal of the Korean Society of Radiology
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• v.12 no.4
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• pp.459-466
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• 2018

Risk of radiation worker and radiation workplace are being mainly assessed by exposure dose. But, the radiation used in radiation workplace and the work environment are different. Because the nondestructive work environment varies depending on the work subject, the existence and nonexistence of shielding board, and so on. So, we need to consider the various factors in effective radiation protection aspect. We conducted a survey of radiation workers with over two years' experience in NDT workplace and heared the thoughts of experts. As a result, radiation source, exposure dose, current status of workplace management, workers with personel dosimetry problem and status of periodic regulatory inspection were chosen as main factors of radiation risk index model. Also, we primarily set weighting factors in order of importance based on questionnaires. Finally, we determined weighting factor for details of main factors through the professional advice. Therefore, we will be able to develop the radiation risk index model for assessing the risk of nondestructive test workplace based on main factors that are selected through this study.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.211-220
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• 2020

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.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.260-268
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• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Journal of the Korean Society of Radiology
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• v.6 no.2
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• pp.93-98
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• 2012

Micro-computed tomography (microCT) is an important tool for preclinical vascular imaging, with micron-level resolution. This non-destructive means of imaging allows for rapid collection of 2D and 3D reconstructions to visualize specimens prior to destructive analysis such as pathological analysis. Objectives. The aim of this study was to suggest a method for ex vivo, postmortem examination of stented arterial segments with microCT. And ex vivo evaluation of stents such as bare metal or drug eluting stents on in-stent restenosis (ISR) in rabbit model was performed. The bare metal stent (BMS) and drug eluting stent (DES, paclitaxel) were implanted in the left or right iliac arteries alternatively in eight New Zealand white rabbits. After 4 weeks of post-implantation, the part of iliac arteries surrounding the stent were removed carefully and processed for microCT. Prior to microCT analysis, a contrast medium was loaded to lumen of stents. All samples were subjected to an X-ray source operating at 50 kV and 200 ${\mu}A$ by using a 3D isotropic resolution. The region of interest was traced and measured by CTAN analytical software. Objects being exposed to radiation had different Hounsfield unit each other with values of approximately 1.2 at stent area, 0.12 ~ 0.17 at a contrast medium and 0 ~ 0.06 at outer area of stent. Based on above, further analyses were performed. As a result, the difference of lengths and volumes between expanded stents, which may relate to injury score in pathological analysis, was not different significantly. Moreover, ISR area of BMS was 1.6 times higher than that of DES, indicating that paclitaxel has inhibitory effect on cell proliferation and prevent infiltration of restenosis into lumen of stent. And ISR area of BMS was higher ($1.52{\pm}0.48mm^2$) than that of DES ($0.94{\pm}0.42mm^2$), indicating that paclitaxel has inhibitory effect on cell proliferation and prevent infiltration of restenosis into lumen of stent. Though it was not statistically significant, it showed that the extent of neointema of mid-region of stents was relatively higher than that of anterior and posterior region in parts of BMS as showing cross-sectional 2-D image. suggest that microCT can be utilized as an accessorial tool for pathological analysis.