• International Journal of CAD/CAM
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• v.6 no.1
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• pp.81-87
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• 2006

This paper describes a method for extracting surfaces from multi-material CT (Computed Tomography) data. Most contouring methods such as Marching Cubes algorithm assume that CT data are composed of only two materials. Some extended methods such as [3, 6] can extract surfaces from the multi-material (non-manifold) implicit representation. However, these methods are not directly applicable to CT data that are composed of three or more materials. There are two major problems that arise from fundamentals of CT. The first problem is that we have to use n(n-1)/2 threshold values for CT data contains n materials and select appropriately one threshold value for each boundary area. The second is that we cannot reconstruct only from CT data in which area three or more materials are adjacent each other. In this paper, we propose a method to solve the problems by using image analysis and demonstrate the effectiveness of the method with application examples construct polygon models from CT data of machine parts.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.41-47
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• 2019

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were $0.905{\pm}0.048$ and $0.908{\pm}0.047$. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.2
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• pp.137-142
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• 2009

Purpose: This study was performed to find the current problems of positron emission tomography/computed tomography(PET/CT) data on CD for inter-hospital transfer. Materials and Methods: The subjects were 746 consecutive $^{18}F$-fluorodeoxyglucose PET/CT data CDs from 56 hospitals referred to our department for image interpretation. The formats and contents of PET/CT data CDs were reviewed and the email questionnaire survey about this was performed. Results: PET/CT data CDs from 21 of 56 hospitals(37.5%) included all transaxial CT and PET images with DICOM standard format which were required for authentic interpretation. PET/CT data from the others included only secondary capture images or fusion PET/CT images. According to this survey, the main reason of limited PET/CT data on CD for inter-hospital transfer was that the data volume of PET/CT was too large to upload to the Picture Archiving and Communication System. Conclusion: The majority of hospitals provided limited PET/CT data on CD for inter-hospital transfer, which could be inadequate for accurate interpretation and clinical decision making. It is necessary to standardize the format of PET/CT data on CD for inter-hospital transfer including all transaxial CT and PET images with DICOM standard format.

• 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.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.82-85
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• 2009

Purpose: The purpose of this study was to evaluate the effect of extended CT field of view (FOV) on PET/CT of Standardized uptake value (SUV) when imaging extends beyond the CT FOV. Materials and Methods: CT images were reconstructed at different FOV sizes (500 and 700 mm). Two sets of CT images were reconstructed from the CT projection data by using two FOV sizes. Twenty patients were used in this study. PET images were reconstructed using attenuation maps with 500 mm CT FOV and 700 mm extended CT FOV images. Region of interests (ROIs) drawn on the PET images. In addition, twenty patients' PET images reconstructed by 500 mm CT FOV and 700 mm extended CT FOV were compared with $SUV_{max}$. Results: When using attenuation maps with 700 mm extended CT FOV, the $SUV_{max}$ analysis of liver (p=0.000), lung (p=0.007), mediastinum (p=0.001) were statistically significant. Conclusions: 700 mm extended CT FOV helps to recover the true activity distribution in the PET emission data. In addition, 700 mm extended CT FOV has affected SUV measurement of liver, lung, mediastinum.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.289-294
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• 2022

In this study, the accuracy and usefulness of volume measurement were investigated as a phantom experiment using CT and USG scan data and a clinical trial using patient scan data. As a result, there was no significant difference between the volume of the actual round phantom of various volumes for both the CT and ultrasound devices (p>0.05). As a result of statistical analysis, it was analyzed that there was no significant difference (p>0.05). Clinical application of this result requires more clinical trials, but if a CT or ultrasound device is selected and applied in consideration of patient radiation exposure, the examiner's scanning technology, and CT reconstruction experience, the basic data in terms of the usefulness of volume measurement using CT scan image is considered to have application value.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.38-40
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• 2010

Purpose: This study was performed to find the current problems of PET/CT data from other hospitals. Materials and Methods: The subjects were acquired from 64 hospitals referred to our department for image interpretation. The formats and contents of PET/CT data were reviewed and the phone questionnaire survey about these were performed. Results: PET/CT data from 39 of 64 hospitals (61%) included all transaxial CT and PET images with DICOM (Digital Imaging Communications in Medicine) standard format which were required for authentic interpretation. PET/CT data from the others included only secondary capture images or fusion PET/CT images. Conclusion: The majority of hospitals provided limited PET/CT data which could be inadequate for accurate interpretation and clinical decision making. It is necessary to standardize the format of PET/CT data to transfer including all transaxial CT and PET images with DICOM standard format.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• Progress in Medical Physics
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• v.18 no.3
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• pp.134-138
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• 2007

The current PET/CT system with high quality CT images not only increases diagnostic value by providing anatomic localization, but also shortens the acquisition time for attenuation correction than primary PET system. All commercially available PET/CT system uses the CT scan for attenuation correction instead of the transmission scan using radioactive source such as $^{137}Cs,\;^{68}Ge$. However the CT scan may substantially increase the patient dose. The purpose of this study was to evaluate quality of PET images reconstructed by CT attenuation map using various tube currents. in this study, images were acquired for 3D Hoffman brain phantom and cylindrical phantom using GE DSTe PET/CT system. The emission data were acquired for 10 min using phantoms after injecting 44.03 MBq of $^{18}F-FDG$. The CT images for attenuation map were acquired by changing tube current from 10 mA to 95 mA with fixed exposure time of 8 sec and fixed tube voltage of 140 kVp. The PET images were reconstructed using these CT attenuation maps. Image quality of CT images was evaluated by measuring SD (standard deviation) of cylindrical phantom which was filled with water and $^{18}F-FDG$ solution. The PET images were evaluated by measuring the activity ratio between gray matter and white matter in Hoffman phantom images. SDs of CT images decrease by increasing tube current. When PET images were reconstructed using CT attenuation maps with various tube currents, the activity ratios between gray matter and white matter of PET images were almost same. These results indicated that the quality of the PET images using low dose CT data were comparable to the PET images using general dose CT data. Therefore, the use of low dose CT is recommended than the use of general dose CT, when the diagnostic high quality CT is not required. Further studies may need to be performed for other system, since this study is limited to the GE DSTe system used in this study.

• Journal of the Korean earth science society
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• v.39 no.4
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• pp.388-400
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• 2018

The purpose of this study was to explore the nine components of computational thinking (CT) practices and their operational definitions from the view of science education and to develop a CT practice framework that is going to be used as a planning and assessing tool for CT practice, as it is required for students to equip with in order to become creative problem solvers in $21^{st}$ century. We employed this framework into the earlier developed STEAM programs to see how it was valid and reliable. We first reviewed theoretical articles about CT from computer science and technology education field. We then proposed 9 components of CT as defined in technology education but modified operational definitions in each component from the perspective of science education. This preliminary CTPF (computational thinking practice framework) from the viewpoint of science education consisting of 9 components including data collection, data analysis, data representation, decomposing, abstraction, algorithm and procedures, automation, simulation, and parallelization. We discussed each component with operational definition to check if those components were useful in and applicable for science programs. We employed this CTPF into two different topics of STEAM programs to see if those components were observable with operational definitions. The profile of CT components within the selected STEAM programs for this study showed one sequential spectrum covering from data collection to simulation as the grade level went higher. The first three data related CT components were dominating at elementary level, all components of CT except parallelization were found at middle school level, and finally more frequencies in every component of CT except parallelization were also found at high school level than middle school level. On the basis of the result of CT usage in STEAM programs, we included 'generalization' in CTPF of science education instead of 'parallelization' which was not found. The implication about teacher education was made based on the CTPF in terms of science education.