• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.537-543
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• 2022

The purpose of this simulation study was to evaluate the possibility of pancreas detection through effective atomic number information using dual-energy computed tomography(CT). The effective atomic number of 10 tissue-equivalent materials were estimated through stoichiometric calibration. For stoichiometric calibration, HU values at low-energy (80 kV) and high-energy (140 kV) for 10 tissue-equivalent materials were used. Based on this method, the effective atomic number image of the tissue-equivalent material was extracted through an iterative algorithm. According to the results, the attenuation ratio in accordance with the effective atomic number was estimated to have an R² value of 0.9999, and the effective atomic number of Pancreas, Water, Liver, Blood, Spongiosa, and Cortical bone was overall within 1% accuracy compared to the theoretical value. Conventional pancreatic cancer examination uses a contrast medium, so there is a possibility of potential side effects of the contrast medium. In order to solve this problem, it is thought that it will be possible to contribute to an accurate and safe examination by extracting the effective atomic number using dual-energy CT without contrast enhancement. Based on this study, future research will be conducted on the detection of pancreatic cancer using the HU value of pancreatic cancer based on clinical images.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1249-1249
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• 2022

The facade, an exterior material of a building, is one of the crucial factors that determine its morphological identity and its functional levels, such as energy performance, earthquake and fire resistance. However, regardless of the type of exterior materials, huge property and human casualties are continuing due to frequent exterior materials dropout accidents. The quality of the building envelope depends on the detailed design and is closely related to the back frames that support the exterior material. Detailed design means the creation of a shop drawing, which is the stage of developing the basic design to a level where construction is possible by specifying the exact necessary details. However, due to chronic problems in the construction industry, such as reducing working hours and the lack of design personnel, detailed design is not being appropriately implemented. Considering these characteristics, it is necessary to develop the detailed design process of exterior materials and works based on the domain-expert knowledge of the construction industry using artificial intelligence (AI). Therefore, this study aims to establish a detailed design automation algorithm for AI-based condition-responsive exterior wall panels and their back frames. The scope of the study is limited to "detailed design" performed based on the working drawings during the exterior work process and "stone panels" among exterior materials. First, working-level data on stone works is collected to analyze the existing detailed design process. After that, design parameters are derived by analyzing factors that affect the design of the building's exterior wall and back frames, such as structure, floor height, wind load, lift limit, and transportation elements. The relational expression between the derived parameters is derived, and it is algorithmized to implement a rule-based AI design. These algorithms can be applied to detailed designs based on 3D BIM to automatically calculate quantity and unit price. The next goal is to derive the iterative elements that occur in the process and implement a robotic process automation (RPA)-based system to link the entire "Detailed design-Quality calculation-Order process." This study is significant because it expands the design automation research, which has been rather limited to basic and implemented design, to the detailed design area at the beginning of the construction execution and increases the productivity by using AI. In addition, it can help fundamentally improve the working environment of the construction industry through the development of direct and applicable technologies to practice.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.234-240
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• 2007

Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a predefined order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.17-24
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• 2011

Purpose: Presently in the nuclear medicine field, the high-speed image reconstruction algorithm like the OSEM algorithm is widely used as the alternative of the filtered back projection method due to the rapid development and application of the digital computer. There is no to relate and if it applies the optimal parameter be clearly determined. In this research, the quality change of the Jaszczak phantom experiment and brain SPECT patient data according to the iteration times and subset number change try to be been put through and analyzed in 3D OSEM reconstruction method of applying 3D beam modeling. Materials and Methods: Patient data from August, 2010 studied and analyzed against 5 patients implementing the brain SPECT until september, 2010 in the nuclear medicine department of ASAN medical center. The phantom image used the mixed Jaszczak phantom equally and obtained the water and 99mTc (500 MBq) in the dual head gamma camera Symbia T2 of Siemens. When reconstructing each image altogether with patient data and phantom data, we changed iteration number as 1, 4, 8, 12, 24 and 30 times and subset number as 2, 4, 8, 16 and 32 times. We reconstructed in reconstructed each image, the variation coefficient for guessing about noise of images and image contrast, FWHM were produced and compared. Results: In patients and phantom experiment data, a contrast and spatial resolution of an image showed the tendency to increase linearly altogether according to the increment of the iteration times and subset number but the variation coefficient did not show the tendency to be improved according to the increase of two parameters. In the comparison according to the scan time, the image contrast and FWHM showed altogether the result of being linearly improved according to the iteration times and subset number increase in projection per 10, 20 and 30 second image but the variation coefficient did not show the tendency to be improved. Conclusion: The linear relationship of the image contrast improved in 3D OSEM reconstruction method image of applying 3D beam modeling through this experiment like the existing 1D and 2D OSEM reconfiguration method according to the iteration times and subset number increase could be confirmed. However, this is simple phantom experiment and the result of obtaining by the some patients limited range and the various variables can be existed. So for generalizing this based on this results of this experiment, there is the excessiveness and the evaluation about 3D OSEM reconfiguration method should be additionally made through experiments after this.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.449-459
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• 2015

The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the $5^{th}$ NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.

• The Korean Journal of Nuclear Medicine
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• v.39 no.1
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• pp.57-68
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• 2005

Purpose: The objective of this study was to assess attenuation correction algorithms with the $^{137}Cs$ point source for the brain positron omission tomography (PET) imaging process. Materials & Methods: Four different types of phantoms were used in this study for testing various types of the attenuation correction techniques. Transmission data of a $^{137}Cs$ point source were acquired after infusing the emission source into phantoms and then the emission data were subsequently acquired in 3D acquisition mode. Scatter corrections were performed with a background tail-fitting algorithm. Emission data were then reconstructed using iterative reconstruction method with a measured (MAC), elliptical (ELAC), segmented (SAC) and remapping (RAC) attenuation correction, respectively. Reconstructed images were then both qualitatively and quantitatively assessed. In addition, reconstructed images of a normal subject were assessed by nuclear medicine physicians. Subtracted images were also compared. Results: ELEC, SAC, and RAC provided a uniform phantom image with less noise for a cylindrical phantom. In contrast, a decrease in intensity at the central portion of the attenuation map was noticed at the result of the MAC. Reconstructed images of Jaszack and Hoffan phantoms presented better quality with RAC and SAC. The attenuation of a skull on images of the normal subject was clearly noticed and the attenuation correction without considering the attenuation of the skull resulted in artificial defects on images of the brain. Conclusion: the complicated and improved attenuation correction methods were needed to obtain the better accuracy of the quantitative brain PET images.

• Progress in Medical Physics
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• v.19 no.1
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• pp.80-88
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• 2008

Recent radiotherapy dose planning system (RTPS) generally adapted the kernel beam using the convolution method for computation of tissue dose. To get a depth and profile dose in a given depth concerened a given photon beam, the energy spectrum was reconstructed from the attenuation dose of transmission of filter through iterative numerical analysis. The experiments were performed with 15 MV X rays (Oncor, Siemens) and ionization chamber (0.125 cc, PTW) for measurements of filter transmitted dose. The energy spectrum of 15MV X-rays was determined from attenuated dose of lead filter transmission from 0.51 cm to 8.04 cm with energy interval 0.25 MeV. In the results, the peak flux revealed at 3.75 MeV and mean energy of 15 MV X rays was 4.639 MeV in this experiments. The results of transmitted dose of lead filter showed within 0.6% in average but maximum 2.5% discrepancy in a 5 cm thickness of lead filter. Since the tissue dose is highly depend on the its energy, the lateral dose are delivered from the lateral spread of energy fluence through flattening filter shape as tangent 0.075 and 0.125 which showed 4.211 MeV and 3.906 MeV. In this experiments, analyzed the energy spectrum has applied to obtain the percent depth dose of RTPS (XiO, Version 4.3.1, CMS). The generated percent depth dose from $6{\times}6cm^2$ of field to $30{\times}30cm^2$ showed very close to that of experimental measurement within 1 % discrepancy in average. The computed dose profile were within 1% discrepancy to measurement in field size $10{\times}10cm$, however, the large field sizes were obtained within 2% uncertainty. The resulting algorithm produced x-ray spectrum that match both quality and quantity with small discrepancy in this experiments.

• Journal of the Korean Society of Radiology
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• v.6 no.5
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• pp.357-363
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• 2012

The purpose of this study on head computed tomography scan corporate reorganization adaptive iteration algorithm using the statistical noise, and quality assessment, reduction of dose was evaluated. Head CT examinations do not apply ASIR group [A group], ASIR 50 applies a group [B group] were divided into examinations. B group of each 46.9 %, 48.2 %, 43.2 %, and 47.9 % the measured in the phantom research result of measurement of CT noise average were reduced more than A group in the central part (A) and peripheral unit (B, C, D). CT number was measured with the quantitive analytical method in the display-image quality evaluation and about noise was analyze. There was A group and difference which the image noise notes statistically between B. And A group was high so that the image noise could note than B group (31.87 HUs, 31.78 HUs, 26.6 HUs, 30.42 HU P<0.05). The score of the observer 1 of A group evaluated 73.17 on 74.2 at the result 80 half tone dot of evaluating by the qualitative evaluation method of the image by the bean curd clinical image evaluation table. And the score of the observer 1 of B group evaluated 71.77 on 72.47. There was no difference (P>0.05) noted statistically. And the inappropriate image was shown to the diagnosis. As to the exposure dose, by examination by applying ASIR 50 % there was no decline in quality of the image, 47.6 % could reduce the radiation dose. In conclusion, if ASIR is applied to the clinical part, it is considered with the dose written much more that examination is possible. And when examination, it is considered that it becomes the positive factor when the examiner determines.

• The Korean Journal of Nuclear Medicine
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• v.37 no.2
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• pp.73-82
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• 2003

Objectives: A new software (Cardiac SPECT Analyzer: CSA) was developed for quantification of volumes and election fraction on gated myocardial SPECT. Volumes and ejection fraction by CSA were validated by comparing with those quantified by Quantitative Gated SPECT (QGS) software. Materials and Methods: Gated myocardial SPECT was peformed in 40 patients with ejection fraction from 15% to 85%. In 26 patients, gated myocardial SPECT was acquired again with the patients in situ. A cylinder model was used to eliminate noise semi-automatically and profile data was extracted using Gaussian fitting after smoothing. The boundary points of endo- and epicardium were found using an iterative learning algorithm. Enddiastolic (EDV) and endsystolic volumes (ESV) and election fraction (EF) were calculated. These values were compared with those calculated by QGS and the same gated SPECT data was repeatedly quantified by CSA and variation of the values on sequential measurements of the same patients on the repeated acquisition. Results: From the 40 patient data, EF, EDV and ESV by CSA were correlated with those by QGS with the correlation coefficients of 0.97, 0.92, 0.96. Two standard deviation (SD) of EF on Bland Altman plot was 10.1%. Repeated measurements of EF, EDV, and ESV by CSA were correlated with each other with the coefficients of 0.96, 0.99, and 0.99 for EF, EDV and ESV respectively. On repeated acquisition, reproducibility was also excellent with correlation coefficients of 0.89, 0.97, 0.98, and coefficient of variation of 8.2%, 5.4mL, 8.5mL and 2SD of 10.6%, 21.2mL, and 16.4mL on Bland Altman plot for EF, EDV and ESV. Conclusion: We developed the software of CSA for quantification of volumes and ejection fraction on gated myocardial SPECT. Volumes and ejection fraction quantified using this software was found valid for its correctness and precision.