• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.173-181
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• 2015

We developed a wireless gamma-ray probe based on radiation photon counting method to diagnose and detect remaining lesions after surgery, and its effectiveness was evaluated using calibration sources and a phantom. The probe was designed and miniaturized using a semi-conductor-based radiation sensor, and a Bluetooth remote communication module was used to implement the wireless diagnosis and detection system. Moreover, a remote monitoring system was implemented to monitor affected areas during diagnosis and surgery. To assess the effectiveness of the developed probe in this study, calibration sources $^{57}Co$, $^{133}Ba$, $^{22}Na$ and $^{137}Cs$ and a chicken breast phantom were used. Furthermore, the probe's detection response to gamma ray was confirmed through evaluation. Its clinical applicability was verified by assessing the response linearity and detection direction according to gamma-ray intensity, as well as the detection efficiency according to the depth of the gamma source in the phantom.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1754-1759
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• 2022

The activity of gamma-ray emitting nuclides is calculated assuming that each gamma-ray is detected individually; thus, the magnitude of the coincidence summing signal must be considered during activity calculations. Here, the correction factor for the coincidence summing effect was calculated, and the detection efficiencies of two HPGe detectors were compared. The CANBERRA Inc. GC4018 high-purity Ge detector provided an estimate for the peak-to-total ratio using a point source to determine the coincidence summing correction factor. The ORTEC Inc. GEM60 high-purity Ge detector uses EFFTRAN in LVis to obtain the parameters of the detector and source model and the gamma-gamma and gamma-X match estimates, in order to determine the coincidence summing correction factor. Nuclide analyses, radioactivity comparisons, and analyses of reference material samples were performed utilizing certified reference materials to accurately determine the detection efficiencies. For both Co-60 and Y-88, the detection efficiency for a point source increased by an average of at least 12-13%, whereas the detection efficiency determined using LVis increased by an average of at least 13-15%. The calculated radioactivity values of the certified reference material and reference material samples were accurate to within 3% and 6% of the measured values, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.197-203
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• 2015

Nuclear power plant has increased continuously for power production in all over the world and the interest about nuclear accident and the dismantling of aging nuclear power plant has been a growing. The leaked radioactive source that is generated by radiation accidents must detect and remove to minimized the damage as soon as possible. Gamma-ray detection system that have been developed until now cannot provide the precise position of radioactive sources because they detect and imaging the position of radiation sources in just two dimensions. In this paper, stereo gamma ray detection system has developed and the algorithm for calculation of the distance has implemented to be able to measure the distribution of the leakage gamma ray source for the system. Stereo camera calibration for distance detection was conducted with the correction pattern and LED light and we carried out performance test of the system for the LED light source and a gamma ray source. In both experiments the results of the performance test, it was confirmed to have a 5% error. The results of this paper is used as a material for the development of gamma-ray imaging device.

• Progress in Superconductivity
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• v.9 no.2
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• pp.162-166
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• 2008

We are developing a sensitive gamma ray spectrometer based on superconducting transition edge sensors. The detector consists of a small piece of high purity Sn as an absorber and a Ti/Au bilayer as a temperature sensor. It is designed to measure the thermal signal caused by absorption of gamma rays. The mechanical support and the thermal contact between the absorber and the thermometer were made with Stycast epoxy. The bilayer was formed by e-beam evaporation and patterned by wet etching on top of a $SiN_X$ membrane. A sharp superconducting transition of the film was measured near 100 mK. When the film was biased to the edge of the transition, signals were observed due to single photon absorption emitted from an $^{241}Am$ source. The measured spectrum showed several characteristic peaks of the source including 59.5 keV gamma line. The full with at half maximum was about 900 eV for the 59.5 keV gamma line. The background was low enough to resolve low energy lines. Considerations to improve the energy resolution of the gamma ray spectrometer are also discussed.

• Food Science and Biotechnology
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• v.18 no.3
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• pp.788-792
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• 2009

Gamma $({\gamma})-irradiation$ can be used to control pathogens such as Vibrio vulnificus in seafood. The effects of irradiation on microbial cell populations (%) have been studied in order to develop detection methods for irradiated foods. The method used in this study was ethidium bromide monoazide (EMA) real-time polymerase chain reaction (PCR), using V. vulnificus specific primer, EMA, and $SYBR^{(R)}$ Green to discriminate between ${\gamma}-irradiated$ and non-irradiated cells. Confocal microscope examination showed that ${\gamma}-irradiation$ damaged portions of the cell membrane, allowing EMA to penetrate cells of irradidated V. vulnificus. ${\gamma}-Irradiation$ at 1.08 KGy resulted in log reduction ($-1.15{\pm}0.13$ log reduction) in genomic targets derived from EMA real-time PCR. The combination cold/heat shock resulted in the highest ($-1.74{\pm}0.1$ log reduction) discrimination of dead irradiated V. vulnificus by EMA real-time PCR.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3844-3853
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• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.400-407
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• 2019

In this paper, the shadow detection and reconstruction method are proposed using intrinsic image, which does not change the essential characteristics under the influence of various illuminance, and multi-scale gamma correction. The shadow detection was estimated by the pixel change information between a grayscale and an intrinsic image of the color image, and the brightness of the image were adjusted by gamma correction in the shadow restoration process. Multi-scale gamma correction is performed for each channel of a color image due to the fact that the saturation can be changed by nonlinear adjustment to individual pixel values. Multi-scale gamma values are estimated based on the information of the crossed edge between shadows and non-shadowed regions in the color image, as a result, the shadows are reconstructed by correcting different region features with multi-scale gamma values. Experimental results show that the proposed method effectively reconstructs shadows in a single natural image.

• Journal of Radiation Protection and Research
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• v.42 no.1
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• pp.48-55
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• 2017

Background: Simultaneous detection of neutrons and gamma rays have become much more practicable, by taking advantage of good gamma-ray discrimination properties using pulse shape discrimination (PSD) technique. Recently, we introduced a commercial CLYC system in Korea, and performed an initial characterization and simulation studies for the CLYC detector system to provide references for the future implementation of the dual-mode scintillator system in various studies and applications. Materials and Methods: We evaluated a CLYC detector with 95% $^6Li$ enrichment using various gamma-ray sources and a $^{252}Cf$ neutron source, with validation of our Monte Carlo simulation results via measurement experiments. Absolute full-energy peak efficiency values were calculated for gamma-ray sources and neutron source using MCNP6 and compared with measurement experiments of the calibration sources. In addition, behavioral characteristics of neutrons were validated by comparing simulations and experiments on neutron moderation with various polyethylene (PE) moderator thicknesses. Results and Discussion: Both results showed good agreements in overall characteristics of the gamma and neutron detection efficiencies, with consistent ~20% discrepancy. Furthermore, moderation of neutrons emitted from $^{252}Cf$ showed similarities between the simulation and the experiment, in terms of their relative ratios depending on the thickness of the PE moderator. Conclusion: A CLYC detector system was characterized for its energy resolution and detection efficiency, and Monte Carlo simulations on the detector system was validated experimentally. Validation of the simulation results in overall trend of the CLYC detector behavior will provide the fundamental basis and validity of follow-up Monte Carlo simulation studies for the development of our dual-particle imager using a rotational modulation collimator.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1189-1196
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• 2015

In this paper, we proposed automatic extraction of brain tumor using morphological operation and statistical tumors size in MR images. Neurosurgery have used gamma-knife therapy by MR images. However, the gamma-knife plan systems needs the brain tumor regions, because gamma-ray should intensively radiate to the brain tumor except for normal cells. Therefore, gamma-knife plan systems spend too much time on designating the tumor regions. In order to reduce the time of designation of tumors, we progress the automatical extraction of tumors using proposed method. The proposed method consist of two steps. First, the information of skull at MRI slices remove using statistical tumors size. Second, the ROI is extracted by tumor feature and average of tumors size. The detection of tumor is progressed using proposed and threshold method. Moreover, in order to compare the effeminacy of proposed method, we compared snap-shot and results of proposed method.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.