• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1849-1854
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• 2009

This paper describes the design, evaluation and implementation of a compensating algorithm for an iron-cored measurement current transformer (CT) that removes the effects of the hysteresis characteristics of the iron-core. The exciting current resulting from the hysteresis characteristics of the core causes an error of the CT. The proposed algorithm decomposes the exciting current into the core loss current and the magnetizing current and each of them is estimated. The core loss current is calculated from the secondary voltage and the voltage-core loss current curve. The core flux linkage is calculated and then inserted into the flux-magnetizing current curve to estimate the magnetizing current. The exciting current at every sampling interval is obtained by summing the core loss and magnetizing currents and then added to the measured current to obtain the correct secondary current. The voltage-core loss current curve and flux-magnetizing current curves, which are different from the conventional curves, are derived in this paper. The performance of the proposed algorithm is validated under various conditions using EMTP generated data. The experimental test results of an iron-core type electronic CT, which consists of the iron-core and the compensation board, are also included. The results indicate that the proposed algorithm can improve the accuracy of the measurement CT significantly, and thus reduce the size and the cost of the CT.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• Journal of Creative Information Culture
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• v.5 no.2
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• pp.183-191
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• 2019

The purpose of this paper is to develop an unplugged education program that develops the 4C (Creativity, Critical thinking, Communication ability, Collaboration) and CT (Computational Thinking) competencies required in modern society. This study discovered "Fine Dust Robot" as a theme suitable for the unplugged education program, and designed the Unplugged 4-hour education program which can develop 4C and CT competencies. The first stage motivates learning, and the second and third stages develop unplugged activity to develop CT. In the fourth stage, the algorithms created through unplugged activities were programmed through the natural language instruction card and produced the output. We developed educational materials that can be utilized in the unplugged education program. Finally, education programs were conducted for elementary school students, and pre- and post-tests of computational thinking were conducted for general students and gifted students. Educational effective was found in both groups.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.182-190
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• 2005

Purpose: There are differences between Standard Uptake Value (SUV) of CT attenuation corrected PET and that of $^{137}Cs$. Since various causes lead to difference of SUV, it is important to know what is the cause of these difference. Since only the X-ray CT and $^{137}Cs$ transmission data are used for the attenuation correction, in Philips GEMINI PET/CT scanner, proper transformation of these data into usable attenuation coefficients for 511 keV photon has to be ascertained. The aim of this study was to evaluate the accuracy in the CT measurement and compare the CT and $^{137}Cs$-based attenuation correction in this scanner. Methods: For all the experiments, CT was set to 40 keV (120 kVp) and 50 mAs. To evaluate the accuracy of the CT measurement, CT performance phantom was scanned and Hounsfield units (HU) for those regions were compared to the true values. For the comparison of CT and $^{137}Cs$-based attenuation corrections, transmission scans of the elliptical lung-spine-body phantom and electron density CT phantom composed of various components, such as water, bone, brain and adipose, were performed using CT and $^{137}Cs$. Transformed attenuation coefficients from these data were compared to each other and true 511 keV attenuation coefficient acquired using $^{68}Ge$ and ECAT EXACT 47 scanner. In addition, CT and $^{137}Cs$-derived attenuation coefficients and SUV values for $^{18}F$-FDG measured from the regions with normal and pathological uptake in patients' data were also compared. Results: HU of all the regions in CT performance phantom measured using GEMINI PET/CT were equivalent to the known true values. CT based attenuation coefficients were lower than those of $^{68}Ge$ about 10% in bony region of NEMA ECT phantom. Attenuation coefficients derived from $^{137}Cs$ data was slightly higher than those from CT data also in the images of electron density CT phantom and patients' body with electron density. However, the SUV values in attenuation corrected images using $^{137}Cs$ were lower than images corrected using CT. Percent difference between SUV values was about 15%. Conclusion: Although the HU measured using this scanner was accurate, accuracy in the conversion from CT data into the 511 keV attenuation coefficients was limited in the bony region. Discrepancy in the transformed attenuation coefficients and SUV values between CT and $^{137}Cs$-based data shown in this study suggests that further optimization of various parameters in data acquisition and processing would be necessary for this scanner.

• Progress in Medical Physics
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• v.19 no.4
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• pp.209-218
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• 2008

According to improved radiation therapy technology such as IMRT and proton therapy, the accuracy of patient alignment system is more emphasized and IGRT is dominated research field in radiation oncology. We proposed to study the feasibility of cone-beam CT system using simple x-ray imaging systems for image guided proton therapy at National Cancer Center. 180 projection views ($2,304{\times}3,200$, 14 bit with 127 ${\mu}m$ pixel pitch) for the geometrical calibration phantom and humanoid phantoms (skull, abdomen) were acquired with $2^{\circ}$ step angle using x-ray imaging system of proton therapy gantry room ($360^{\circ}$ for 1 rotation). The geometrical calibration was performed for misalignments between the x-ray source and the flat-panel detector, such as distances and slanted angle using available algorithm. With the geometrically calibrated projection view, Feldkamp cone-beam algorithm using Ram-Lak filter was implemented for CBCT reconstruction images for skull and abdomen phantom. The distance from x-ray source to the gantry isocenter, the distance from the flat panel to the isocenter were calculated as 1,517.5 mm, 591.12 mm and the rotated angle of flat panel detector around x-ray beam axis was considered as $0.25^{\circ}$. It was observed that the blurring artifacts, originated from the rotation of the detector, in the reconstructed toomographs were significantly reduced after the geometrical calibration. The demonstrated CBCT images for the skull and abdomen phantoms are very promising. We performed the geometrical calibration of the large gantry rotation system with simple x-ray imaging devices for CBCT reconstruction. The CBCT system for proton therapy will be used as a main patient alignment system for image guided proton therapy.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.196-204
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• 2016

World Health Organization reported that heart-related diseases such as coronary artery stenoses show the highest occurrence rate which may cause heart attack. Using Computed Tomography angiography images will allow radiologists to detect and have intervention by creating 3D roadmapping of the vessels. However, it is often complex and difficult do reconstruct 3D vessel which causes very large amount of time and previous researches were studied to segment vessels more accurate automatically. Therefore, in this paper, Region Competition, Geodesic Active Contour (GAC), Multi-atlas based segmentation and Active Shape Model algorithms were applied to segment aortic root from CTA images and the results were analyzed by using mean Hausdorff distance, volume to volume measure, computational time, user-interaction and coronary ostium detection rate. As a result, Extracted 3D aortic model using GAC showed the highest accuracy but also showed highest user-interaction results. Therefore, it is important to improve automatic segmentation algorithm in future

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.37-44
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• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• Journal of the Korean Society of Radiology
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• v.10 no.2
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• pp.81-87
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• 2016

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some fatty liver patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of fatty liver. As the results of examining over 30 example CT images of fatty liver, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including Average Gray Level, Entropy 96.67%, Skewness 93.33%, and Smoothness while others showed a little low disease recognition rate: 83.33% for Uniformity 86.67% and for Average Contrast 80%. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of fatty liver and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.81-86
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• 2012

Purpose : Combined MR/PET scanners that use the MRI for PET AC face the challenge of absent surface coils in MR images and thus cannot directly account for attenuation in the coils. To make up for the weak point of MR attenuation correction, Three Modality System (PET/CT +MR) were used in Severance hospital. The goal of this work was to investigate the effects of MR Torso Coil on CT attenuation correction for PET. Materials and Methods : PET artifacts were evaluated when the MR Torso Coil was present of CTAC data with changing various kV and mA in uniformity water phantom and 1994 NEMA cylinderical phantom. They evaluated and compared the following two scenarios: (1) The uniform cylinder phantom and the MR Torso Coil scanned and reconstructed using CT-AC; (2) 1994 NEMA cylinderical phantom and the MR Torso Coil scanned and reconstructed using CT-AC. Results : Streak artifacts were present in CT images containing the MR Torso Coil due to metal components. These artifacts persisted after the CT images were converted for PET-AC. CT scans tended to over-estimate the linear attenuation coefficient when the kV and mA is increasing of the metal components when using conventional methods for converting from CT number. Conclusion : The presence of MR coils during PET/CT scanning can cause subtle artifacts and potentially important quantification errors. Alternative CT techniques that mitigate artifacts should be used to improve AC accuracy. When possible, removing segments of an MR coil prior to the PET/CT exam is recommended. Further, MR coils could be redesigned to reduce artifacts by rearranging placement of the most attenuating materials.