• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1041-1043
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• 2015

본 논문은 저선량 흉부 CT 영상을 활용하여 패결절을 자동으로 검출하는 알고리즘에 관한 연구내용을 담고 있다. 폐 결절 자동 검출을 위해 폐 CT 영상 내에 폐결절의 가지고 있는 특징들 중, 이동성 및 기하학적 특징을 가지고 폐혈관과 폐결절을 구분하였다. 실험한 영상은 폐결절이 없는 정상환자군을 가지고 실시 하였으며, 그 결과 4.4False Positive / Scan이 발생하였다.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.115-116
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• 2015

Grid voltage variation is usually happened but existing PV inverter dose not consider this variation before inverting into AC power. This inverting is a cause of unnecessary losses. This paper proposes that DC-link should be changed for removing those losses. Furthermore, maximum efficiency tracking control algorithm with variable DC-link voltage is proposed and verified by simulation and experiment.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.166-168
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• 2015

Line of Balance (LOB) method is suitable to schedule construction projects composed of repetitive activities. Since existing LOB based repetitive project scheduling methods are deterministic, they do not lend themselves to handle uncertainties involved in repetitive construction process. Indeed, existing LOB scheduling dose not handle variability of project performance indicators. In order to bridge the gap between reality and estimation, this study provides a stochastic LOB based scheduling method that allows schedulers for effectively dealing with the uncertainties of a construction project performance. The proposed method retrieves an appropriate probability distribution function (PDF) concerning project completion times, and determines favorable start times of activities. A case study is demonstrated to verify and validate the capability of the proposed method in a repetitive construction project planning.

• Journal of IKEEE
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• v.21 no.2
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• pp.146-149
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• 2017

In this paper, we propose a wide-range radiation measurement system using GM Tube and NaI(TI) detector. The proposed system is designed as a small module optimized to control and count the detector signal of NaI(Tl) Detector and GM Tube. The radiation dose is measured in a wide-range 0.1uSv/h to 10mSv/h in conjunction with two detectors, and two detectors operate simultaneously at 10uSv/h to 100uSv/h, where the measurement interval overlaps. The radiation dose was selected using a wide-range radiation measurement algorithm that controls the on/off function of the detector in the appropriate interval for the overlapped radiation measurable interval. In order to evaluate the performance of the proposed system, it has been confirmed that the measurement uncertainty of each section is measured as ${\pm}7.5%$ and it operates normally under ${\pm}15%$ of the international standard.

• Journal of Radiation Protection and Research
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• v.44 no.4
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• pp.149-155
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• 2019

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixel-to-pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

• Toxicological Research
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• v.32 no.4
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• pp.289-300
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• 2016

Non-genotoxic carcinogens are substances that induce tumorigenesis by non-mutagenic mechanisms and long term rodent bioassays are required to identify them. Recent studies have shown that transcription profiling can be applied to develop early identifiers for long term phenotypes. In this study, we used rat liver expression profiles from the NTP (National Toxicology Program, Research Triangle Park, USA) DrugMatrix Database to construct a gene classifier that can distinguish between non-genotoxic carcinogens and other chemicals. The model was based on short term exposure assays (3 days) and the training was limited to oxidative stressors, peroxisome proliferators and hormone modulators. Validation of the predictor was performed on independent toxicogenomic data (TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System, Osaka, Japan). To build our model we performed Random Forests together with a recursive elimination algorithm (VarSelRF). Gene set enrichment analysis was employed for functional interpretation. A total of 770 microarrays comprising 96 different compounds were analyzed and a predictor of 54 genes was built. Prediction accuracy was 0.85 in the training set, 0.87 in the test set and increased with increasing concentration in the validation set: 0.6 at low dose, 0.7 at medium doses and 0.81 at high doses. Pathway analysis revealed gene prominence of cellular respiration, energy production and lipoprotein metabolism. The biggest target of toxicogenomics is accurately predict the toxicity of unknown drugs. In this analysis, we presented a classifier that can predict non-genotoxic carcinogenicity by using short term exposure assays. In this approach, dose level is critical when evaluating chemicals at early time points.

• Progress in Medical Physics
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• v.31 no.4
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• pp.163-171
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• 2020

Purpose: In this study, the accuracies of electron Monte Carlo (eMC) calculation algorithms were evaluated to determine whether electron beams were modeled by optional air profiles (APs) designed for each applicator size. Methods: Electron beams with the energies of 6, 9, 12, and 16 MeV for VitalBeam (Varian Medical System, Palo Alto, CA, USA) and 6, 9, 12, 16, and 20 MeV for Clinac iX (Varian Medical System) were used. Optional APs were measured at the source-to-detector distance of 95 cm with jaw openings appropriate for each machine, electron beam energy, and applicator size. The measured optional APs were postprocessed and converted into the w2CAD format. Then, the electron beams were modeled and calculated with and without optional APs. Measured profiles, percentage depth doses, penumbras with respect to each machine, and energy were compared to calculated dose distributions. Results: For VitalBeam, the profile differences between the measurement and calculation were reduced by 0.35%, 0.15%, 0.14%, and 0.38% at 6, 9, 12, and 16 MeV, respectively, when the beams were modeled with APs. For Clinac iX, the differences were decreased by 0.16%, -0.31%, 0.94%, 0.42%, and 0.74%, at 6, 9, 12, 16, and 20 MeV, respectively, with the insertion of APs. Of note, no significant improvements in penumbra and percentage depth dose were observed, although the beam models were configured with APs. Conclusions: The accuracy of the eMC calculation can be improved in profiles when electron beams are modeled with optional APs.

• Journal of Radiation Protection and Research
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• v.45 no.4
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• pp.171-177
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• 2020

Background: This study aims to determine the effective atomic number (Zeff) from dual-energy image sets obtained using a conventional computed tomography (CT) simulator. The estimated Zeff can be used for deriving the stopping power and material decomposition of CT images, thereby improving dose calculations in radiation therapy. Materials and Methods: An electron-density phantom was scanned using Philips Brilliance CT Big Bore at 80 and 140 kVp. The estimated Zeff values were compared with those obtained using the calibration phantom by applying the Rutherford, Schneider, and Joshi methods. The fitting parameters were optimized using the nonlinear least squares regression algorithm. The fitting curve and mass attenuation data were obtained from the National Institute of Standards and Technology. The fitting parameters obtained from stopping power and material decomposition of CT images, were validated by estimating the residual errors between the reference and calculated Zeff values. Next, the calculation accuracy of Zeff was evaluated by comparing the calculated values with the reference Zeff values of insert plugs. The exposure levels of patients under additional CT scanning at 80, 120, and 140 kVp were evaluated by measuring the weighted CT dose index (CTDIw). Results and Discussion: The residual errors of the fitting parameters were lower than 2%. The best and worst Zeff values were obtained using the Schneider and Joshi methods, respectively. The maximum differences between the reference and calculated values were 11.3% (for lung during inhalation), 4.7% (for adipose tissue), and 9.8% (for lung during inhalation) when applying the Rutherford, Schneider, and Joshi methods, respectively. Under dual-energy scanning (80 and 140 kVp), the patient exposure level was approximately twice that in general single-energy scanning (120 kVp). Conclusion: Zeff was calculated from two image sets scanned by conventional single-energy CT simulator. The results obtained using three different methods were compared. The Zeff calculation based on single-energy exhibited appropriate feasibility.

• Journal of Medicine and Life Science
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• v.20 no.2
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• pp.83-88
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• 2023

Metal artifacts cause inaccuracies in target delineation, radiation treatment planning, and delivery when computed tomography images of a radiotherapy patient implanted with a high-density material in the body are acquired. In this study, we investigated the possibility of obtaining improved images in clinical trials through metal artifact reduction using silicon impression materials without the need for a specific metal artifact reduction algorithm. A silicon impression material exhibiting a constant Hounsfield unit (HU) value according to the mixing ratio of the catalysts and bases was selected. The material did not exhibit any change in weight or shape over time. For both the instances of inserting the metal material and applying the silicon impression material, the HU value and dose were compared with homogeneous cases filled with water-equivalent materials. When the silicon impression material was applied to the region where the high-density material was located, the HU value was within 5% and the dose was within 3% compared with those of the homogeneous cases. In this study, the silicon impression materials reduced metal artifacts. However, because the composition, shape, size, and location of high-density materials differ, further studies are required to consider these factors in clinical applications.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3140-3149
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• 2023

In theory, the sharp dose falloff at the distal end of a proton beam allows for high conformal dose to the target. However, conformity has not been fully achieved in practice, primarily due to beam range uncertainty, which is approximately 4% and varies slightly across institutions. To address this issue, we developed a new range verification system prototype: a multi-slit prompt-gamma camera (MSPGC). This system features high prompt-gamma detection sensitivity, an advanced range estimation algorithm, and a precise camera positioning system. We evaluated the range measurement precision of the prototype for single spot beams with varying energies, proton quantities, and positions, as well as for spot-scanning proton beams in a simulated SSPT treatment using a phantom. Our results demonstrated high accuracy (<0.4 mm) in range measurement for the tested beam energies and positions. Measurement precision increased significantly with the number of protons, achieving 1% precision with 5 × 10⁸ protons. For spot-scanning proton beams, the prototype ensured more than 5 × 10⁸ protons per spot with a 7 mm or larger spot aggregation, achieving 1% range measurement precision. Based on these findings, we anticipate that the clinical application of the new prototype will reduce range uncertainty (currently approximately 4%) to 1% or less.