In Recent years, there has been a noticeable increase in the global incidence of breast cancer, with approximately 2.3 million cases of female breast cancer reported worldwide in 2020. Numerous studies are currently underway to enhance the accuracy of breast cancer diagnosis through the development of digital mammography detectors. This study aims to create Monte Carlo simulation-based mammographic anti-scatter grids and investigate their utility in evaluating the performance of digital mammography detector. Two types of mammographic anti-scatter grids, MAM-CP and Senographe 600T HF, were created using Monte Carlo simulation software (MCNPX 2.7.0), with grid ratios of 3.7 : 1 and 5 : 1, respectively. The grid physical characteristics (sensitivity, exposure factor, contrast improvement ratio) were calculated based on the KS C IEC60627 in the simulations using two X-ray qualities, RQA-M2 (28 kVp) and MW4 (35 kVp). As the X-ray tube voltage increased from 28 kVp to 35 kVp, sensitivity and exposure factor exhibited a decreasing trend, while contrast improvement ratio demonstrated an increasing trend. With an increase in grid ratio from 3.7 : 1 to 5 : 1, all physical characteristics showed an upward trend. Our results were consistent with a previous study that conducted measurements of physical properties using a real phantom. However, the pattern of change in the contrast improvement ratio with X-ray tube voltage differed from the previous study.

Medical institutions wishing to install and operate diagnostic radiation generators must complete appointment training within one year of appointment based on the 「Medical Act」 and the 「Rules on Safety Management of Diagnostic Radiation Generator Devices」 which will come into effect on January 1, 2024. Additionally, You must receive supplementary education every three years from the date you received it. The strengthening of safety management for diagnostic radiation generators used in medical institutions means that although the radiation exposure that may occur when using diagnostic radiation generators is low, the risk of carcinogenesis may be higher than previously evaluated. In addition, safety management of diagnostic radiation generators can be said to be an essential requirement because it has been reported that the incidence of leukemia and other diseases is increasing in diagnostic radiation tests. However, the safety management training targets and programs for radiation exposure management operated by other organizations other than diagnostic radiation generators are significantly different. In addition, since the public institutions that are responsible for radiation safety management are divided, there is a risk of duplicative, excessive, and under-administrative application to medical institutions and educational institutions that install and operate diagnostic radiation generators. Therefore, we would like to determine their consistency by comparing domestic and foreign related cases and the provisions of the 「Medical Act」 and the 「Nuclear Safety Act」.

Control of scattered radiation is one of very important factors in the use of medical radiation. In general X-ray exam, the causes, measurement methods, and the kind of detectors of scattered rays within the radiation area are diverse. In this study, the dose of scattered ray was measured by changing the thickness of the polycarbonate phantom and the tube voltage. As a result of measurement of scattered radiation, the results show that the scattered dose significantly(p<.05) increased with growing of thickness of phantom in the tube voltage 40, 50 and 60 kVp(F(p)<.05, R²>64%). As tube voltage increased at all phantom thicknesses, the scattered dose also significantly(p<.05) increased(F(p)<.05, R²>69%). In cases where a significant correlation was shown, the coefficient of determination of more than 60% was shown in regression analysis. The results of this study can be used as data on scattered radiation dose according to the tube voltage and the object thickness in general X-ray imaging exam.

This study was purpose to quantitative evaluation of comparison of the image intensity uniformity and noise power spectrum (NPS) by using American college of radiology (ACR) phantom for magnetic resonance imaging (MRI). The MRI was used achiva 3.0T MRI and discovery MR 750, 3.0T, the head and neck matrix shim SENSE head coil were 32 channels receive MR coil. The MRI was used parameters of image sequence for ACR standard and general hospital. NPS value of the ACR standard T2 vertical image in GE equipment was 7.65E-06 when the frequency was 1.0 mm^-1. And the NPS value of the ACR hospital T1 region of interest (ROI) 9 over all vertical image in Philips equipment was 9E-08 when the frequency was 1.0 mm^-1 and the NPS value of the hospital T2 ROI 9 over all vertical image in Philips equipment was 1.06E-07 when the frequency was 1.0 mm^-1. NPS was used efficiently by using a general hospital vertical sequence more than the standard vertical sequence method by using the ACR phantom. Furthermore NPS was the quantitative quality assurance (QA) assessment method for noise and image intensity uniformity characteristics was applied mutatis mutandis, and the results values of the physical imaging NPS of the 3.0T MRI and ACR phantom were presented.

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

In this study, we optimized the FNLM algorithm through a simulation study and applied it to a phantom scanned by low-dose CT to evaluate whether the FNLM algorithm can be used to obtain improved image quality images. We optimized the FNLM algorithm with MASH phantom and FASH phantom, which the algorithm was applied with MATLAB, increasing the smoothing factor from 0.01 to 0.05 with increments of 0.001 and measuring COV, RMSE, and PSNR values of the phantoms. For both phantom, COV and RMSE decreased, and PSNR increased as the smoothing factor increased. Based on the above results, we optimized a smoothing factor value of 0.043 for the FNLM algorithm. Then we applied the optimized FNLM algorithm to low dose lung CT and lung CT under normal conditions. In both images, the COV decreased by 55.33 times and 5.08 times respectively, and we confirmed that the quality of the image of low dose CT applying the optimized FNLM algorithm was 5.08 times better than the image of lung CT under normal conditions. In conclusion, we found that the smoothing factor of 0.043 among the factors of the FNLM algorithm showed the best results and validated the performance by reducing the noise in the low-quality CT images due to low dose with the optimized FNLM algorithm.