• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1671-1680
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• 2014

There are always much concern about the leakage of radiation materials in the event of dismantle or unexpected accident of nuclear power plant. In order to remove the leakage of radiation materials, appropriate dispersion detection techniques for radiation materials are necessary. However, because direct handling of radiation materials is highly restricted and risky, developing radiation-related techniques needs computer simulation in advance to evaluate the feasibility. In this paper, we propose a radiation imaging technique which can acquire 3D dispersion information of radiation materials and tested by simulation. Using two virtual 1D radiation sensors, we obtain stereo radiation images and acquire the 3D depth to virtual radiation materials using stereo disparity. For point and plane type virtual radiation materials, the possibility of the acquisition of stereo radiation image and 3D information are simulated.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.

• Journal of radiological science and technology
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• v.40 no.1
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• pp.71-78
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• 2017

Scattered radiation is inherent phenomenon of x-ray, which occurs to the subject (or patient). Therefore it cannot be avoidable but also interacts as serious noise factor because the only meaningful information on x-ray radiography is primary x-ray photons. The purpose of this study was to quantify scattered radiation for various shooting parameters and to verify the effect of anti-scatter grid. We employed beam stopper method to characterize scatter to primary ratio. To evaluate effect on the projection images calculated contrast to noise ratio of given shooting parameters. From the experiments, we identified the scattered radiation increases in thicker patient and smaller air gap. Moreover, scattered radiation degraded contrast to noise ratio of the projection images. We find out that the anti-scatter grid rejected scattered radiation effectively, however there were not fewer than 100% of scatter to primary ratio in some shooting parameters. The results demonstrate that the scattered radiation was serious problem of medical x-ray system, we confirmed that the scattered radiation was not considerable factor of dig ital radiog raphy.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.372-374
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• 2006

방사선을 센싱하는 센서들에는 어려 종류가 있으며, 그 센서에 따라서 감도나 센싱방식이 다양하다. 본 연구에서는 해상도가 높은 범용 CCD 센서를 이용하여 방사선을 검출하는데, 들어오는 영상에서 방사선에 반응하여 생기는 백색량을 처리하여 방사선량을 측정하고 공간상에서 방사선이 가장 많이 나오는 포인터를 제시하며 스테레오 카메라 방식의 구현으로 방사선원까지의 거리 산출할 수 있다. 추후 영상을 합성하여 입체화 시키는 연구도 진행할 예정이다. 논 연구의 결과물로 예기치 않은 방사선 사고의 경우 효과적인 사친처리에 활용될 수 있을 것이다.

• The Journal of Korean Society for Radiation Therapy
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• v.14 no.1
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• pp.15-22
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• 2002

1. 목적 : 방사선종양학과에서 3차원 치료계획용 전산화단층촬영 시 조영제 주입율에 따른 CT 값(hounsfield unit, H.U) 변화를 정량적으로 평가하여 최적의 영상증강효과 및 방사선치료계획을 위한 기초 프로토콜을 제시하고자 한다. 2. 대상 및 방법: 연세암센터 방사선종양학과에서 3차원 치료계획용 전산화단층촬영을 시행한 상복부(폐암)환자 20명을 대상으로 하였다. 조영제 양(130mL)은 일정하게 고정하였고, 조영제주입율을 1.2, 1.5, 2.0 mL/sec로 변화시켜가며 조영제를 주입하면서 3차원 방사선치료계획영상에 적합한 조영제주입율(contrast flow rate)과 지연시간(delay time)을 도출하였고, CT 값을 측정하여 정량적 평가를 시행하였다. 관심부위는 폐동맥과 폐정맥으로 하였다. 그리고, 환자 기본정보, 조영제주입율, H.U 등 영상증강에 영향을 미치는 인자들을 통계처리 프로그램인 SPSS를 이용하여 최적의 영상을 획득할 수 있는 기초 프로토콜을 작성하였다. 3. 결과 : 폐암환자의 전산화단층촬영영상 획득 시 3차원 방사선치료계획에 적합한 영상을 얻을 수 있는 조건중 조영제주입율은 1.5 mL/sec 이었고, 지연시간은 $60{\sim}70$초이었다. 통계처리를 수행한 결과 환자의 기본정보 및 조영제주입율 등이 영상증강에 영향을 미치는 인자임을 알 수 있었다. 본 연구에서 작성한 기초 프로토콜을 이용하여 3차원 방사선치료계획 시 정확한 종양 및 정상조직 설정이 용이하게 되어 방사선치료 효율을 극대화 할 수 있었다. 4. 결론 : 방사선종양학과에서 3차원 치료계획용 전산화단층촬영 시 사용할 수 있는 기초적인 영상획득 프로토콜을 도출하였고, 향후 더 많은 임상경험과 정량적 평가가 수반된다면 임상에 적극 사용할 수 있을 것이라 사료된다.

• Korean Journal of Digital Imaging in Medicine
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• v.7 no.1
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• pp.23-31
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• 2005

원격진료 홈네트워크 아파트 진료용 키오스크 모바일주치의 등으로 대표되는 U-헬스케어에 있어서 기초가 되는 것은 의료정보를 디지털화해서 전자적 자료의 형태로 저장 보관하고 이를 송 수신할 수 있는 기술이라고 할 수 있다. 우리나라의 경우, U-Korea 전략의 하나로 보건복지부가 주축이 되어 2005년 10월 현재 국가보건의료정보화계획(ISP)을 수립하기 위한 작업을 추진중에 있다. 여기서, 예컨대 임상병리검사소견이나 방사선촬영소견 등의 의료정보가 전자적 장치에 의해 디지털화 할 경우 디지털 의료정보가 되는 것이며, 이 가운데 특히 방사선촬영소견 등 방사선분야의 모든 촬영기록이 PACS시스템을 통해 기재되거나 저장 전송될 경우 이를 디지털 의료영상정보라고 할 수 있다. 그런데 오늘날 정보통신기술의 발달로 말미암아 디지털 의료영상정보를 포함한 디지털의료정보는 대량적으로 수집 저장되고 유통 내지 공동활용이 보편화되어 감에 따라 그 의료정보의 보호에 관한 문제가 중요한 이슈로 대두되고 있다. 결론적으로 말하자면, 이러한 디지털 의료영상정보가 전자의무기록(EMR) 형태로 저장 보관되는 경우 이는 전자의무기록에 관한 법률규정이 적용되어 법률적 보호를 받게 되며, 그 보호의 강도는 종래 오프라인 상의 의료정보 보호보다 한층 강화된 규정을 두고 있다. 이와 같은 흐름에 있어서 최근 정부가 국가보건의료정보화계획 수립과 함께 제정작업을 추진하고 있는 가칭 의료정보화촉진 및 개인정보보호에 관한 법률(안)은 시사점이 크다고 보기 때문에 소개하고자 한다.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.625-631
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• 2023

The important requirement of industrial dynamic X-ray detector operating under high tube voltage up to 450 kVp for 24 hours and 7 days is to obtain significantly high radiation resistance. This study presents the radiation resistance characteristics of various thin film transistors (TFTs) with a-Si, poly-Si and IGZO semiconducting layers. IGZO TFT offering dozens of times higher field effect mobility than a-Si TFT was processed with highly hydrogenated plasma in between IGZO semiconducting layer and inter-layered dielectric. The hydrogenated IGZO TFT showed most sustainable radiation resistance up to 10,000Gy accumulated, thus, concluded that it is a sole switching device in X-ray imaging sensor offering dynamic X-ray imaging at high frame rate under extremely severe radiation environment such as automated X-ray inspection.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.811-813
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• 2013

In order to measure the radiation, there are types of sensors plurality. I was using the detection method and sensitivity of the CCD sensor in the scintillator and collimator in the sensor. In this study, in order to detect radiation using a CCD sensor with high resolution, by measuring the radiation dose by processing the visible light generated in response to radiation of the image coming into the CCD in the scintillator in space it is to present a pointer that radiation comes out most. It is intended to imaging by calculation of the distance to the radiation source to the implementation of the stereo camera system video in the future.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.305-314
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• 2023

With the development of medical technology and radiation treatment equipment, the frequency of high-precision radiation therapy such as intensity modulation radiation therapy has increased. Image-guided radiation therapy has become essential for radiation therapy in precise and complex treatment plans. In particular, with the introduction of imaging equipment for diagnosis in a linear accelerator, CBCT scanning became possible, which made it possible to calibrate and correct the patient's posture through 3D images. Although more precise reproduction of the patient's posture has become possible, the exposure dose delivered to the patient during the image acquisition process cannot be ignored. Radiation optimization is necessary in the field of radiation therapy, and efforts to reduce exposure are necessary. However, when acquiring 3D CBCT images by changing the imaging conditions to reduce exposure, there should be no image quality or artefacts that would make it impossible to align the patient's position. In this study, Rando phantom was used to scan and evaluate images for each shooting condition. The highest SNR was obtained at 100 kV 80 mA 25 ms F1 filter 180°. As the tube voltage and tube current increased, the noise decreased, and the bowtie filter showed the optimal effect at high tube current. Based on the actual scanned images, it was confirmed that patient alignment was possible under all imaging conditions, and that image-guided radiation therapy for patient alignment was possible under the condition of 70 kV 10 mA 20 ms F0 filter 180°, which showed the lowest SNR. In this study, image evaluation was conducted according to the imaging conditions, and low tube voltage, tube current, and small rotation angle scan are expected to be effective in reducing radiation exposure. Based on this, the patient's exposure dose should be kept as low as possible during CBCT imaging.