• Progress in Medical Physics
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• v.22 no.3
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• pp.148-154
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• 2011

The quality factors ($k_{Q,Q_0}$) were evaluated by appling the results recently studied for the effect of central electrode in TRS-398 protocol. The PTW-31010 and IBA-CC13 chambers were used in this study. The quality factors were calculated as a function of beam quality for high energy electron and photon beams and compared with data currently used in TRS-398 protocol. In the PTW-31010 chamber using aluminium electrode, appling the new central electrode collections, the quality factors were 0.4% and 0.9% higher than current TRS-398 data for high energy photon and electron beams respectively. In the IBA-CC13 chamber using C-552 electrode, there are no variations in quality factors compared to TRS-398 data currently used.

• Journal of the Korean Society of Radiology
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• v.2 no.4
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• pp.5-9
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• 2008

Antiscatter grids are widely used in radiography to remove scattered X-rays and thus improve the image contrast. However, the use of grids makes moir$\acute{e}$ artifact in the digital image, and this can be a critical reason for a mistaken diagnosis. In this paper, we examined that moire artifacts are how to relate with grid frequency, pixel pitch and grid rotation angle. To experiment we prepared 6 grids having different line frequencies (4.0 to 8.5lines/mm) and tested with a DR imager having a $139{\mu}m{\times}139{\mu}m$ pixel size. In the result of this experiment, we could get data about moir$\acute{e}$ artifact that could be make solution to remove the line artifact for the successful use of the grid in digital radiography. The acquired data and theory through this experiment, are expected to make contribution to the elimination of moir$\acute{e}$ artifact in the DR system.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.1-6
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• 2022

Deep learning is a collection of algorithms that enable learning by summarizing the key contents of large amounts of data; it is being developed to diagnose lesions in the medical imaging field. To evaluate the accuracy of the cerebral hemorrhage diagnosis, we used a convolutional neural network (CNN) to derive the diagnostic accuracy of cerebral parenchyma computed tomography (CT) images and the cerebral parenchyma CT images of areas where cerebral hemorrhages are suspected of having occurred. We compared the accuracy of CNN with different numbers of hidden layers and discovered that CNN with more hidden layers resulted in higher accuracy. The analysis results of the derived CT images used in this study to determine the presence of cerebral hemorrhages are expected to be used as foundation data in studies related to the application of artificial intelligence in the medical imaging industry.

• Progress in Medical Physics
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• v.32 no.4
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• pp.99-106
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• 2021

Purpose: In this study, we aimed to manufacture a patient-specific gel phantom combining three-dimensional (3D) printing and polymer gel and evaluate the radiation dose and dose profile using gel dosimetry. Methods: The patient-specific head phantom was manufactured based on the patient's computed tomography (CT) scan data to create an anatomically replicated phantom; this was then produced using a ColorJet 3D printer. A 3D polymer gel dosimeter called RTgel-100 is contained inside the 3D printing head phantom, and irradiation was performed using a 6 MV LINAC (Varian Clinac) X-ray beam, a linear accelerator for treatment. The irradiated phantom was scanned using magnetic resonance imaging (Siemens) with a magnetic field of 3 Tesla (3T) of the Korea Institute of Nuclear Medicine, and then compared the irradiated head phantom with the dose calculated by the patient's treatment planning system (TPS). Results: The comparison between the Hounsfield unit (HU) values of the CT image of the patient and those of the phantom revealed that they were almost similar. The electron density value of the patient's bone and brain was 996±167 HU and 58±15 HU, respectively, and that of the head phantom bone and brain material was 986±25 HU and 45±17 HU, respectively. The comparison of the data of TPS and 3D gel revealed that the difference in gamma index was 2%/2 mm and the passing rate was within 95%. Conclusions: 3D printing allows us to manufacture variable density phantoms for patient-specific dosimetric quality assurance (DQA), develop a customized body phantom of the patient in the future, and perform a patient-specific dosimetry with film, ion chamber, gel, and so on.

• Journal of Biomedical Engineering Research
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• v.19 no.4
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• pp.351-360
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• 1998

Digital gamma camera has many advantages over analog gamma camera. These include convenient quality control, easy calibration and operation, and possible image quantitation which results in improving diagnostic accuracies. The digital data can also be utilized for telemedicine and picture archiving and communication system. However, many hospitals still operate analog cameras and have difficult situation to replace them with digital cameras. We have studied a feasibility of digitalizing an analog gamma camera into a digital camera using Gamma-PF interface board. The physical characteristics that we have measured are spatial resolution, sensitivity, uniformity, and image contrast. The patient's data obtained for both analog and digital camera showed very similar image quality. The results suggest that it may be feasible to upgrade an analog camera into a digital gamma camera in clinical environments.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.111-116
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• 2022

In this study, the applicability of machine learning for the development of a simulator for general X-ray examination education is evaluated. To this end, k-nearest neighbor(kNN), support vector machine(SVM) and neural network(NN) classification models are analyzed to present the most suitable model by analyzing the results. Image data was obtained by taking 100 photos each corresponding to Posterior anterior(PA), Posterior anterior oblique(Obl), Lateral(Lat), Fan lateral(Fan lat). 70% of the acquired 400 image data were used as training sets for learning machine learning models and 30% were used as test sets for evaluation. and prediction model was constructed for right-handed PA, Obl, Lat, Fan lat image classification. Based on the data set, after constructing the classification model using the kNN, SVM, and NN models, each model was compared through an error matrix. As a result of the evaluation, the accuracy of kNN was 0.967 area under curve(AUC) was 0.993, and the accuracy of SVM was 0.992 AUC was 1.000. The accuracy of NN was 0.992 and AUC was 0.999, which was slightly lower in kNN, but all three models recorded high accuracy and AUC. In this study, right-handed PA, Obl, Lat, Fan lat images were classified and predicted using the machine learning classification models, kNN, SVM, and NN models. The prediction showed that SVM and NN were the same at 0.992, and AUC was similar at 1.000 and 0.999, indicating that both models showed high predictive power and were applicable to educational simulators.

• Journal of radiological science and technology
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• v.40 no.4
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• pp.557-565
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• 2017

This study was conducted to investigate the radiation protection, awareness, and attitude of radiological technologists in the mammography room due to the low energy use of breast imaging. Data collection was performed by 222 independent radiological technologists in the breast and breast clinic of six hospitals except Jeju Island. The independent sample t-test and oneway variance analysis were performed. As a result, it was found that the lower the number of men(p<0.05), the age of 30s(p<0.05), the marriage(p<0.05), the work experience of more than 10 years(p<0.05), the working hours of 8 hours(p<0.01), the less the impact of radiation(p<0.01), the more important it was to radiation defense. appear. This is thought to be influenced by the work specificity and work environment of the mammography room, and it will be used as a basic data to raise awareness and act on the exposure through analysis with other factors.

• Korean Journal of Radiology
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• v.21 no.9
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• pp.1027-1035
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• 2020

Objective: To assess the appropriate follow-up interval, and rate and timepoint of cancer detection in women with Breast Imaging Reporting and Data System (BI-RADS) 3 lesions on screening ultrasonography (US) according to the type of institution. Materials and Methods: A total of 1451 asymptomatic women who had negative or benign findings on screening mammogram, BI-RADS 3 assessment on screening US, and at least 6 months of follow-up were included. The median follow-up interval was 30.8 months (range, 6.8-52.9 months). The cancer detection rate, cancer detection timepoint, risk factors, and clinicopathological characteristics were compared between the screening and tertiary centers. Nominal variables were compared using the chi-square or Fisher's exact test and continuous variables were compared using the independent t test or Mann-Whitney U test. Results: In 1451 women, 19 cancers (1.3%) were detected; two (0.1%) were diagnosed at 6 months and 17 (1.2%) were diagnosed after 12.3 months. The malignancy rates were both 1.3% in the screening (9 of 699) and tertiary (10 of 752) centers. In the screening center, all nine cancers were invasive cancers and diagnosed after 12.3 months. In the tertiary center, two were ductal carcinomas in situ and eight were invasive cancers. Two of the invasive cancers were diagnosed at 6 months and the remaining eight cancers newly developed after 13.1 months. Conclusion: One-year follow-up rather than 6-month follow-up may be suitable for BI-RADS 3 lesions on screening US found in screening centers. However, more caution is needed regarding similar findings in tertiary centers where 6-month follow-up may be more appropriate.

• Korean Journal of Radiology
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• v.24 no.12
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• pp.1306-1308
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• 2023

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.177-179
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• 2002

We have developed a scheduling system for heavy ion radiotherapy considering the condition of three treatment rooms and treatment planning for each patient. This system consists of a database (patient information, treatment method and machine schedule), a schedule for radiotherapy and WEB server. All operation of this system, such as data input, to change and to view the schedule, are performed by using a WEB browser. In order to protect personal information for the patients, access privilege to each information are limited by according to the occupational category. This system is connected with a hospital central information management system (AMIDAS) and an irradiation-managing computer for the heavy ion radiotherapy. A basic information for the patient is got from AMIDAS and the daily schedule sends to the treatment control computer at each treatment room through the irradiation-managing computer every morning. The daily, weekly, monthly schedules in the treatment room and the treatment condition of each patient are shared on the WEB browser with the all participants of the heavy ion therapy. This system could be useful to save a time to generate a treatment schedule and to inform us the most up-to-date treatment schedule and the related information at the same time.