• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.676-684
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• 2000

In this study, behavior of $C_t$ which is a well-known fracture parameter characterizing creep crack growth rate, is investigated for weld interface cracks. Finite element analyses were per formed for a C(T) specimen under constant loading condition for elastic-plastic-creeping materials. In modeling C(T) geometry, an interface was employed along the crack plane which simulated the interface between weld and base metals. The $C_t$ versus time relations were obtained under various creep constant combinations and plastic constant combinations for weld and base metals, respectively. A unified $C_t$ versus time curve is obtained by normalizing $C_t$ with $C^*$ and t with $t_T$ for all the cases of material constant variations.

• Journal of the Korean Society of Radiology
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• v.11 no.6
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• pp.501-508
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• 2017

The dependence of CT scanning parameters on the CT number to physical density conversion from the CT image of CT and CBCT electron density phantom acquired by the CT scanner using in radiotherapy were analyzed by experiment. The CT numbers were independent of the tube current product exposure time, slice thickness, filter of image reconstruction, field of view and volume of phantom. But the CT numbers were dependent on the tube voltage and cross section of phantom. As a result, for physical density range above 0, the maximum CT number difference observed at the tube voltage between 90 and 120 kVp was 27%, and the maximum CT number difference observed between CT body and head electron density phantom was 15%.

• 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 Korean Journal of Nuclear Medicine
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• v.31 no.1
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• pp.83-89
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• 1997

About one-third of radiologically indeterminate solitary pulmonary nodules (SPN) are eventually turned out to be malignant. It is very important to noninvasively determine whether the SPN is malignant or not for the decision of its way of management. PET imaging is highlighted by its unique ability of imaging the function and metabolism of cells. Glucose metabolism is increased in malignant transformed cells. We peformed FDG-PET studies in patients who had radiologically indeterminate SPN and compared the findings with histologic diagnoses to assess the diagnostic accuracy in the detection of malignancy and to decide which parameter is the most suitable for clinical practice among peak SUV (pSUV), average SUV (aSUV), 50/10 ratio, and time-activity curve (TAC), Thirty patients were included in this study and the most useful parameter was pSUV. The sensitivity and specificity in the detection of malignant SPN using 3.5 as a cut off pSUV were both 87%. Interestingly, all 2 false-negative cases were bronch-ioloalveolar carcinoma on histologic examination. If these cases, which could be strongly suspected by CT findings, were excluded, the sensitivity of pSUV was 100%. In conclusion, PET imaging is very helpful for determining malignancy in indeterminate SPN and pSUV is a conveniently measurable parameter which is valuable for interpretation.

• Journal of radiological science and technology
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• v.34 no.2
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• pp.149-156
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• 2011

This study is to identify perceptions and response degrees of anxiety for each factor, targeting patients for CT test and analyze the relations between factors. It is to provide scientific fundamental data to reduce anxiety by improving awareness of patients about CT test by analyzing relations between variables. The subjects of this study were surveyed in self-writing type, targeting 263 patients for CT test in the department of radiology at three University hospitals from July to September, 2010. This survey was executed once by a structured self-administered survey type. The targeting patients for CT test of anxiety will investigate for affect. Anxiety by each CT test variables depending on CT test-related features showed independent variable is Expense Responsibility, Economic burden, Sufficient explain, Explain agent, Endoscope, Biopsy, Pre treatment, Previous experience, CT side effect experience, Side effect of contrast medium and dependent variable is physical, Hospital staff, Hospital environment, Socioeconomic These used statistics program SPSS (ver. 13.0). Summarizing the above results of this study, awareness of anxiety and response to it in each variable under CT test appeared significant differences in economic burdens, state anxiety, pre-treatment anxiety, exposure anxiety to radiation, and anxiety of side effect. Therefore, pre-treatment before test and pre-training programs on chemical poison of contrast medium and side effect seem to be able to release patients' anxiety level for CT test. Ways to meditate these anxiety variables and reduce degree of anxiety are needed to be researched more and updated. In addition, impact of patients' economic burdens on CT test anxiety is required to be recognized and solved in society level.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• Journal of radiological science and technology
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• v.33 no.4
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• pp.355-362
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• 2010

This study determines a range of CT parameter values in Brain CT which are minimizing patient absorption dose without compromising the image quality and optimal exposure condition. We measured dose and image noise using conventional CT parameters in Brain CT. In additon, we evaluated dose, SNR and PSNR of head phantom images while changing kVp and rotation time. In this study, effectiveness of dose that was achieved from dose reproducible experiments in conventional head CT condition is determined by changing kVp and rotation time. Dose and PSNR is related to low dose-high resolution condition. In conclusion, we suggest that using proposed conditions is effective for imaging to compare with conditions proposed by the manufacturer.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.499-511
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• 2019

The preceding study of this study developed puzzle-based computer science education programs to enhance the computational thinking of elementary school students over 1 to 3 times. The preceding study then applied such programs into the field, categorized the effects of education into CT creativity and CT cognitive ability to improve the education programs. Based on the results of these preceding studies, the hierarchical Bayesian inference modeling was performed using age and CT thinking ability as parameters. From the results, this study predicted the effectiveness of puzzle-based computer science education programs in middle and high schools and proposed major improvement areas and directions for puzzle-based computer science education programs that are to be deployed in the future throughout middle and high schools.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.1
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• pp.87-92
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• 2016

In this paper, we improved classification performance of benign and malignant lung nodules by including the parenchyma features. For small pulmonary nodules (4-10mm) nodules, there are a limited number of CT data voxels within the solid tumor, making them difficult to process through traditional CAD(computer aided diagnosis) tools. Increasing feature extraction to include the surrounding parenchyma will increase the CT voxel set for analysis in these very small pulmonary nodule cases and likely improve diagnostic performance while keeping the CAD tool flexible to scanner model and parameters. In AdaBoost learning using naive Bayes and SVM weak classifier, a number of significant features were selected from 304 features. The results from the COPDGene test yielded an accuracy, sensitivity and specificity of 100%. Therefore proposed method can be used for the computer aided diagnosis effectively.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.1-9
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• 2023

For optimal image quality of computer tomography angiography (CTA), different iodine concentrations and scan parameters were applied to quantitatively evaluate the image quality characteristics of filtered back projection (FBP), hybrid-iterative reconstruction (hybrid-IR), and deep learning reconstruction (DLR). A 320-row-detector CT scanner scanned a phantom with various iodine concentrations (1.2, 2.9, 4.9, 6.9, 10.4, 14.3, 18.4, and 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm. Data obtained using each reconstruction technique was analyzed through noise, coefficient of variation (COV), and root mean square error (RMSE). As the iodine concentration increased, the CT number value increased, but the noise change did not show any special characteristics. COV decreased with increasing iodine concentration for FBP, adaptive iterative dose reduction (AIDR) 3D, and advanced intelligent clear-IQ engine (AiCE) at various tube voltages and tube currents. In addition, when the iodine concentration was low, there was a slight difference in COV between the reconstitution techniques, but there was little difference as the iodine concentration increased. AiCE showed the characteristic that RMSE decreased as the iodine concentration increased but rather increased after a specific concentration (4.9 mg/mL). Therefore, the user will have to consider the characteristics of scan parameters such as tube current and tube voltage as well as iodine concentration according to the reconstruction technique for optimal CTA image acquisition.