• 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 the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Investigative Magnetic Resonance Imaging
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• v.12 no.1
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• pp.8-19
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• 2008

Purpose : To investigate the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar nuclear Overhauser effect/enhancement (NOE) interaction through 2D- correlation spectroscopy (COSY) and 2D- NOE spectroscopy (NOESY) techniques. Materials and Methods : All 2D experiments were performed on Bruker Avance 500 (11.8 T) with the zshield gradient triple resonance cryoprobe at 298 K. Human brain metabolites were prepared with 10% $D_2O$. Two-dimensional spectra with 2048 data points contains 320 free induction decay (FID) averaging. Repetition delay was 2 sec. The Top Spin 2.0 software was used for post-processing. Total 7 metabolites such as N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), lutamine (Gln), glutamate (Glu), myo-inositol (Ins), and lactate (Lac) were included for major target metabolites. Results : Symmetrical 2D-COSY and 2D-NOESY pectra were successfully acquired: COSY cross peaks were observed in the only 1.0-4.5 ppm, however, NOESY cross peaks were observed in the 1.0-4.5 ppm and 7.9 ppm. From the result of the 2-D COSY data, cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methylene protons (CH2(3,$H{\alpha}$)) at 2.50ppm and methylene protons ($CH_2$,(3,$H_B$)) at 2.70 ppm were observed in NAA. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. From the result of 2-D NOESY data, cross peaks between the NH proton at 8.00 ppm and methyl protons ($CH_3$) were observed in NAA. Cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methyl protons (CH3) at 3.03 ppm and methylene protons (CH2) at 3.93 ppm were observed in Cr. Cross peaks between the methylene protons ($CH_2$(3)) at 2.11 ppm and methylene protons ($CH_2$(4)) at 2.35 ppm, and between the methylene protons($CH_2$ (3)) at 2.11 ppm and methine proton (CH(2)) at 3.76 ppm were observed in Glu. Cross peaks between the methylene protons (CH2 (3)) at 2.14 ppm and methine proton (CH(2)) at 3.79 ppm were observed in Gln. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. Conclusion : The present study demonstrated that in vitro 2D-COSY and NOESY represented the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar NOE interaction. This study could aid in better understanding the interactions between human brain metabolites in vivo 2DCOSY study.

• Journal of radiological science and technology
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• v.31 no.1
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• pp.17-23
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• 2008

Purpose : Because there is a difference depending on the environment as for an inspection equipment the important part of bone density scan and the precision/accuracy of a tester, the management of quality must be made systematically. The equipment failure caused by overload effect due to the aged equipment and the increase of a patient was made frequently. Thus, the replacement of equipment and additional purchases of new bonedensity equipment caused a compatibility problem in tracking patients. This study wants to know whether the clinical changes of patient's bonedensity can be accurately and precisely reflected when used it compatiblly like the existing equipment after equipment replacement and expansion. Materials and methods : Two equipments of GE Lunar Prodigy Advance(P1 and P2) and the Phantom HOLOGIC Spine Road(HSP) were used to measure equipment precision. Each device scans 20 times so that precision data was acquired from the phantom(Group 1). The precision of a tester was measured by shooting twice the same patient, every 15 members from each of the target equipment in 120 women(average age 48.78, 20-60 years old)(Group 2). In addition, the measurement of the precision of a tester and the cross-calibration data were made by scanning 20 times in each of the equipment using HSP, based on the data obtained from the management of quality using phantom(ASP) every morning (Group 3). The same patient was shot only once in one equipment alternately to make the measurement of the precision of a tester and the cross-calibration data in 120 women(average age 48.78, 20-60 years old)(Group 4). Results : It is steady equipment according to daily Q.C Data with $0.996\;g/cm^2$, change value(%CV) 0.08. The mean${\pm}$SD and a %CV price are ALP in Group 1(P1 : $1.064{\pm}0.002\;g/cm^2$, $%CV=0.190\;g/cm^2$, P2 : $1.061{\pm}0.003\;g/cm^2$, %CV=0.192). The mean${\pm}$SD and a %CV price are P1 : $1.187{\pm}0.002\;g/cm^2$, $%CV=0.164\;g/cm^2$, P2 : $1.198{\pm}0.002\;g/cm^2$, %CV=0.163 in Group 2. The average error${\pm}$2SD and %CV are P1 - (spine: $0.001{\pm}0.03\;g/cm^2$, %CV=0.94, Femur: $0.001{\pm}0.019\;g/cm^2$, %CV=0.96), P2 - (spine: $0.002{\pm}0.018\;g/cm^2$, %CV=0.55, Femur: $0.001{\pm}0.013\;g/cm^2$, %CV=0.48) in Group 3. The average error${\pm}2SD$, %CV, and r value was spine : $0.006{\pm}0.024\;g/cm^2$, %CV=0.86, r=0.995, Femur: $0{\pm}0.014\;g/cm^2$, %CV=0.54, r=0.998 in Group 4. Conclusion: Both LUNAR ASP CV% and HOLOGIC Spine Phantom are included in the normal range of error of ${\pm}2%$ defined in ISCD. BMD measurement keeps a relatively constant value, so showing excellent repeatability. The Phantom has homogeneous characteristics, but it has limitations to reflect the clinical part including variations in patient's body weight or body fat. As a result, it is believed that quality control using Phantom will be useful to check mis-calibration of the equipment used. A value measured a patient two times with one equipment, and that of double-crossed two equipment are all included within 2SD Value in the Bland - Altman Graph compared results of Group 3 with Group 4. The r value of 0.99 or higher in Linear regression analysis(Regression Analysis) indicated high precision and correlation. Therefore, it revealed that two compatible equipment did not affect in tracking the patients. Regular testing equipment and capabilities of a tester, then appropriate calibration will have to be achieved in order to calculate confidential BMD.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Progress in Medical Physics
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• v.22 no.1
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• pp.42-51
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• 2011

Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.

• The Journal of Engineering Geology
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• v.19 no.4
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• pp.509-515
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• 2009

Three earthquakes with local magnitude ($M_L$) greater than 3.0 occurred on April 24, June 2 and September 12 in 1999 nearby the Gyeongju area. Redetermined epicenters were located within the radius of 1 km. We carried out waveform inversion analysis to estimate focal mechanism of June 2 event, and P and S wave polarity and their amplitude ratio analysis to estimate focal mechanisms of April 24 and September 12 events. June 2 and September 12 events had similar fault plane solutions each other. The fault plane solution of April 24 event included those of other 2 events, but its distribution range was relatively broad. Focal mechanisms of those events had a strike slip faulting with a small normal component. P-axes of those events were ENE-WSW which were similar to previous studies on the P-axis of the Korean Peninsula. Considering distances between epicenters, similarities of seismic waves and sameness of polarities of seismic data recorded at common seismic stations, these events might occurred at the same fault. The seismic moment of June 2 event was estimated to be $3.9\;{\times}\;10^{14}\;N{\cdot}m$ and this value corresponded to the moment magnitude ($M_W$) 3.7. The moment magnitude estimated by spectral analysis was 3.8, which was similar to that estimated by waveform inversion analysis. The average stress drop was estimated to be 7.5 MPa. Moment magnitudes of April 24 and September 12 events were estimated to be 3.2 and 3.4 by comparing the spectrum of those events recorded at common single seismic station.

• Journal of Radiation Protection and Research
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• v.37 no.4
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• pp.181-190
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• 2012

Naturally occurring radioactive materials (NORM) in building materials are main sources of external radiation exposure to the general public. The objective of this study was to assess external radiation dose in Korean dwellings due to NORM in concrete walls. Reference room model for dose assessment was made by analyzing room structure and housing scale of Korean dwellings. In addition, dose assessments were made for varying room sizes. Absorbed doses to air and effective dose rates were calculated using radiation transport code MCNPX. Assuming a reference room of $3{\times}4{\times}2.8m^3$, absorbed dose rates in air were 0.80, 0.97, 0.08 nGy $h^{-1}$ per Bq $kg^{-1}$ for uranium series, thorium series, and $^{40}K$, respectively. Effective dose rates were 0.57, 0.69, 0.058 nSv $h^{-1}$ per Bq $kg^{-1}$, respectively. Radiation dose resulting from concrete of ceiling and floor increased with room area while radiation dose from concrete of walls decreased with room area. Therefore, total radiation doses were almost the same for the varying room area from 5 to $30m^2$. Effective dose in Korean dwellings was calculated based on measurement data of NORM concentration in concrete and occupancy fraction of Korean population by location. Annual effective dose was 0.59 mSv assuming that indoor occupancy fraction was 0.89 and concentrations of uranium series, thorium series and $^{40}K$ were 26, 39, 596 Bq $kg^{-1}$, respectively. Finally, annual effective dose in Korean dwellings can be calculated by the following equation: Effective dose=indoor occupancy fraction${\times}8760\;h\;y^{-1}{\times}(0.57C_U+0.69C_{Th}+0.058C_K)$.

• Nuclear Medicine and Molecular Imaging
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• v.40 no.1
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• pp.33-39
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• 2006

Purpose: To determine appropriate sampling frequency and time of multiple blood sampling dual exponential method with $^{99m}Tc$-DTPA for calculating glomerular filtration rate (GFR). Materials & Methods: Thirty four patients were included in this study. Three mCi of $^{99m}Tc$-DTPA was intravenously injected and blood sampling at 9 different times, 5ml each, were done. Using the radioactivity of serum, measured by gamma counter, the GFR was calculated using dual exponential method and corrected with the body surface area. Using spontaneously chosen 2 data points of serum radioactivity, 15 collections of 2-sample GFR were calculated. And 10 collections of 3-sample GFR and 12 collections of 4-sample GFR were also calculated. Using the 9-sample GFR as a reference value, degree of agreement was analyzed with Kendall's $\tau$ correlation coefficients, mean difference and standard deviation. Results: Although some of the 2-sample GFR showed high correlation coefficient, over or underestimation had evolved as the renal function change. The 10-120-240 min 3-sample GFR showed a high correlation coefficient (${\tau}=0.93$), minimal difference ($Mean{\pm}SD=-1.784{\pm}3.972$), and no over or underestimation as the renal function changed. The 4-sample GFR showed no better accuracy than the 3-sample GFR. Conclusions: In the wide spectrum of renal function, the 10-120-240 min 3-sample GFC could be the best choice for estimating the patients' renal function.

• Nuclear Engineering and Technology
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• v.28 no.2
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• pp.118-128
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• 1996

The lattice parameters of stoichiometric $UO_2$ and $U_{1-y}Er_{y}O_2$ in the range of y=0.01 to y =0.33 were determined with use of X-ray diffraction data. Oxygen potentials have been measured by means of a thermogravimetric method in the range of 1200~$1500^{\circ}C$ and $10^{-14}$ $\leq$ $Po_2$ $\leq$ $10^{-3}$ for pure $UO_2$ and $U_{1-y}Er_{y}O_{2{\pm}x}$ solid solutions with y=0.02, y=0.06 and y=0.20, respectively. Their oxygen partial pressures were maintained by controlling $CO_2$/CO mixture atmosphere, and the $Po_2$ values corresponding to x of $U_{1-y}Er_{y}O_{2{\pm}x}$ solid solutions were measured with an electrolyte oxygen sensor. The lattice parameter decreases linearly with an increase in the erbium content. The change of the lattice parameter can be expressed in a linear equation of y as a($\AA$) =5.4695-0.220y for 0 $\leq$y$\leq$0.33. The experimental coefficient of y -0.220 in $U_{1-y}Er_{y}O_2$ was an intermediate value between the calculated values -0.273 and -0.156 in the case of $U^{5+}$ and $U^{6+}$, respectively. The (equation omitted) has been found to undergo abrupt increase in the range of -360 to -270 kJ/mole for y=0.06 and -320 to -220 H/mole for y=0.20, respectively, in the temperature range of 1200-$1500^{\circ}C$. (equation omitted) increases with erbium content, but the effect of the dopant for x =0.01 is less significant than that for stoichiometry. The oxygen potentials for $UO_2$ and $U_{0.98}Er_{0.02}O_{2+x}$ can be approximately represented by the $U^{5+}$/$U^{4+}$ model but those for y$\geq$ 0.06 in $U_{1-y}Er_{y}O_{2{\pm}x}$ solid solutions cannot be interpreted by the mean uranium valence model.