• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.71-79
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• 2021

Currently, the naked eyes-based diagnosis of bone metastases on CT images relies on qualitative assessment. For this reason, there is a great need for a state-of-the-art approach that can assess and follow-up the bone metastases with quantitative biomarker. Radiomics can be used as a biomarker for objective lesion assessment by extracting quantitative numerical values from digital medical images. In this study, therefore, we evaluated the clinical applicability of non-invasive and objective bone metastases computer-aided diagnosis using radiomics-based biomarkers in CT. We employed a total of 21 approaches consist of three-classifiers and seven-feature selection methods to predict bone metastases and select biomarkers. We extracted three-dimensional features from the CT that three groups consisted of osteoblastic, osteolytic, and normal-healthy vertebral bodies. For evaluation, we compared the prediction results of the classifiers with the medical staff's diagnosis results. As a result of the three-class-classification performance evaluation, we demonstrated that the combination of the random forest classifier and the sequential backward selection feature selection approach reached AUC of 0.74 on average. Moreover, we confirmed that 90-percentile, kurtosis, and energy were the features that contributed high in the classification of bone metastases in this approach. We expect that selected quantitative features will be helpful as biomarkers in improving the patient's survival and quality of life.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.279-280
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• 2007

Airway wall thickness is an important bio-marker for evaluation of pulmonary diseases such as stenosis, bronchiectasis. Nevertheless, an image-based analysis of the airway tree can provide precise and valuable airway size information, quantitative measurement of airway wall thickness in CT images involves various sources of error and uncertainty. So we have developed an accurate airway wall measurement technique for small airways with three-dimensional (3-D) approach. To illustrate performance of these techniques, we used airway phantom that consisted of 4 acryl tubes with various inner and outer diameters. Results show that evaluation of interpolation and deconvolution methods of airways in 3-D CT images, and significant improvement over the full-width-half-maximum method for measurement of not only location of the luminal and outer edge of the airway wall but airway wall thickness.

• Journal of the Korean Society of Safety
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• v.17 no.4
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• pp.38-44
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• 2002

The application of fractal concept provides an useful method in the study for the quantitative analysis of irregular variations like the fracture surfaces and crack profiles. Fractal curves have characteristics that represents a self-similarity based on the fractal dimension. The fractal dimensions were obtained by the box counting method. In this report, we obtained the nearly stable fractal dimensions of fracture crack profiles for STS316 with CT specimen as the crack advances and the relationships between crack length and fractal dimension. Moreover fractal fracture parameter that corresponds to J-R curve is shown by the relationships between fractal dimension and crack extension. From the results, we concluded that crack extension of high toughness material also shows the fractal characteristics, which can be used in order to evaluate the crack life precisely.

• Journal of Korean Neurosurgical Society
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• v.54 no.5
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• pp.384-389
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• 2013

Objective : Use of quantitative computed tomography (CT) to evaluate bone mineral density was suggested in the 1970s. Despite its reliability and accuracy, technical shortcomings restricted its usage, and dual-energy X-ray absorptiometry (DXA) became the gold standard evaluation method. Advances in CT technology have reduced its previous limitations, and CT evaluation of bone quality may now be applicable in clinical practice. The aim of this study was to determine if the Hounsfield unit (HU) values obtained from CT correlate with patient age and bone mineral density. Methods : A total of 128 female patients who underwent lumbar CT for back pain were enrolled in the study. Their mean age was 66.4 years. Among them, 70 patients also underwent DXA. The patients were stratified by decade of life, forming five age groups. Lumbar vertebrae L1-4 were analyzed. The HU value of each vertebra was determined by averaging three measurements of the vertebra's trabecular portion, as shown in consecutive axial CT images. The HU values were compared between age groups, and correlations of HU value with bone mineral density and T-scores were determined. Results : The HU values consistently decreased with increasing age with significant differences between age groups (p<0.001). There were significant positive correlations (p<0.001) of HU value with bone mineral density and T-score. Conclusion : The trabecular area HU value consistently decreases with age. Based on the strong positive correlation between HU value and bone mineral density, CT-based HU values might be useful in detecting bone mineral diseases, such as osteoporosis.

• Journal of radiological science and technology
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• v.41 no.6
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• pp.553-560
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• 2018

PET-CT and PET-MRI which integrates CT using ionized radiation and MRI using phenomena of magnetic resonance are determined to have the limitation to apply the semi-quantitative index, standardized uptake value (SUV), with the same level due to the fundamental differences of image capturing principle and reorganization, hence, their correlations were analyzed to provide their clinical information. To 30 study subjects maintaining pre-treatment, $^{18}F-FDG$ (5.18 MBq/㎏) was injected and they were scanned continuously without delaying time using $Biograph^{TM}$ mMR 3T (Siemens, Munich) and Biograph mCT 64 (Siemens, Germany), which is an integral type, under the optimized condition except the structural differences of both scanners. Upon the measurement results of $SUV_{max}$ setting volume region of interest with evenly distributed radioactive pharmaceuticals by captured images, $SUV_{max}$ mean values of PET-CT and PET-MRI were $2.94{\pm}0.55$ and $2.45{\pm}0.52$, respectively, and the value of PET-MRI was measured lower by $-20.85{\pm}7.26%$ than that of PET-CT. Also, there was a statistically significant difference in SUVs between two scanners (P<0.001), hence, SUV of PET-CT and PET-MRI cannot express the clinical meanings in the same level. Therefore, in case of the patients who undergo cross follow-up tests with PET-CT and PET-MRI, diagnostic information should be analyzed considering the conditions of SUV differences in both scanners.

• Journal of the Korean Society of Radiology
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• v.84 no.4
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• pp.900-910
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• 2023

Purpose To assess normal CT scans with quantitative CT (QCT) analysis based on smoking habits and chronic obstructive pulmonary disease (COPD). Materials and Methods From January 2013 to December 2014, 90 male patients with normal chest CT and quantification analysis results were enrolled in our study [non-COPD never-smokers (n = 38) and smokers (n = 45), COPD smokers (n = 7)]. In addition, an age-matched cohort study was performed for seven smokers with COPD. The square root of the wall area of a hypothetical bronchus of internal perimeter 10 mm (Pi10), skewness, kurtosis, mean lung attenuation (MLA), and percentage of low attenuation area (%LAA) were evaluated. Results Among patients without COPD, the Pi10 of smokers (4.176 ± 0.282) was about 0.1 mm thicker than that of never-smokers (4.070 ± 0.191, p = 0.047), and skewness and kurtosis of smokers (2.628 ± 0.484 and 6.448 ± 3.427) were lower than never-smokers (2.884 ± 0.624, p = 0.038 and 8.594 ± 4.944, p = 0.02). The Pi10 of COPD smokers (4.429 ± 0.435, n = 7) was about 0.4 mm thicker than never-smokers without COPD (3.996 ± 0.115, n = 14, p = 0.005). There were no significant differences in MLA and %LAA between groups (p > 0.05). Conclusion Even on normal CT scans, QCT showed that the airway walls of smokers are thicker than never-smokers regardless of COPD and it preceded lung parenchymal changes.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.243-247
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• 2012

Beam hardening artifact happens in the CT image. when a PET/CT is conducted while there is a metallic dental implant. The artifact appears in the CT image can affect the PET image. When the patient with head and neck cancer has a metallic dental implant, Beam hardening artifact which was taken in th CT image can change the PET image and SUV value. Therefore, by Quantitative measure of the SUV according to the change in HU by the metallic dental implant, the appropriacy in the clinical application was assessed. The records of 47 patients with PET/CT August 2011. For the analysis, 2 region of interest were defined in area where CT and PET image. As a result of the experiment, if there in an implant, the HU and the SUV increased and there existed a statistically significant difference(p<0.01). Although this level of increase was not large compared with that in the patient who have no metallic dental implant, when a person has head and neck cancer, it is even more likely to be overestimated when diagnosing the cancer. When conducting PET/CT for the patient who have head and neck cancer, the physical biological parts should be considered in order not to make an error in decoding.

• Korean Journal of Digital Imaging in Medicine
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• v.9 no.1
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• pp.21-30
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• 2007

Our objective was to evaluate the CT attenuation coefficient and noise of spatial domain filtering as an alternative to additional image reconstruction using different kernels in abdominal CT. Derived from thin collimated source images was generated using abdomen B10 (very smooth), B20 (smooth), B30 (medium smooth), B40 (medium), B50 (medium sharp), B60 (sharp), B70 (very sharp) and B80 (ultra sharp) kernels. Quantitative CT coefficient and noise measurements provided comparable HU (hounsfield) units in this respect. CT attenuation coefficient (mean HU) values in the abdominal were 60.4$\sim$62.2 HU and noise (7.6$\sim$63.8 HU) in the liver parenchyma. In the stomach a mean (CT attenuation coefficient) of -2.2$\sim$0.8 HU and noise (10.1$\sim$82.4 HU) was measured. Image reconstructed with a convolution kernel led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image medications of image sharpness and noise eliminate the need for reconstruction using different kernels in the future. CT images increase the diagnostic accuracy may be controlled by adjusting CT various kernels, which should be adjusted to take into account the kernels of the CT undergoing the examination.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.709-715
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• 2023

In this study, the performance evaluation of image quality for noise reduction was implemented using the U-net deep learning architecture in computed tomography (CT) images. In order to generate input data, the Gaussian noise was applied to ground truth (GT) data, and datasets were consisted of 8:1:1 ratio of train, validation, and test sets among 1300 CT images. The Adagrad, Adam, and AdamW were used as optimizer function, and 10, 50 and 100 times for number of epochs were applied. In addition, learning rates of 0.01, 0.001, and 0.0001 were applied using the U-net deep learning model to compare the output image quality. To analyze the quantitative values, the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. Based on the results, deep learning model was useful for noise reduction. We suggested that optimized hyper parameters for noise reduction in CT images were AdamW optimizer function, 100 times number of epochs and 0.0001 learning rates.