• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.346-346
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• 1998

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3943-3948
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• 2022

Single-photon emission computed tomography is one of the reliable pin-by-pin verification techniques for spent-fuel assemblies. One of the challenges with this technique is to increase the total fuel assembly verification speed while maintaining high verification accuracy. The aim of the present study, therefore, was to develop an artificial intelligence (AI) algorithm-based tomographic image analysis technique for partial-defect verification of fuel assemblies. With the Monte Carlo (MC) simulation technique, a tomographic image dataset consisting of 511 fuel-rod patterns of a 3 × 3 fuel assembly was generated, and with these images, the VGG16, GoogLeNet, and ResNet models were trained. According to an evaluation of these models for different training dataset sizes, the ResNet model showed 100% pattern estimation accuracy. And, based on the different tomographic image qualities, all of the models showed almost 100% pattern estimation accuracy, even for low-quality images with unrecognizable fuel patterns. This study verified that an AI model can be effectively employed for accurate and fast partial-defect verification of fuel assemblies.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.506-508
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• 2002

As an advancement of medical imaging modalities and analyzing software with multi-function, active researches to acquire high contrast and high resolution image being done. In recently, development of medical imaging modalities like as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) is aiming to display anatomical structure more accuracy and faster. Thus, one of the important areas in CT today is the use of CT scanner for the quantitative evaluation of 3-D reconstruction images from 2-D tomographic images. In CT system, the effective slice thickness and the quality of 3-D reconstructed image will be influenced by imaging acquisition parameters (e.g. pitch and scan mode). In diagnosis and surgical planning, the accurate distance measurements of 3-D anatomical structures play an important role and the accuracy of distance measurements will depend on the acquisition parameters such as slice thickness, pitch, and scan mode. The skull phantom was scanned with SDCT for various acquisition parameters and acquisition slice thicknesses were 3 and 5 mm, and reconstruction intervals were 1, 2, and 3 mm to each pitch. 3-D visualizations and distance measurements were performed with PC based 3-D rendering and analyzing software. Results showed that the image quality and the measurement accuracy of 3-D SDCT images are independent to the reconstruction intervals and pitches.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.553-559
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• 2010

By applying the various reformation techniques by using a circle raw data of after computed tomography image in the patient enforcing the spine screw fixation, this research tried to look into the various information including the exact location of the position of the screw spike and accuracy of an operation. In a clinical, by applying the or multi planar reformatting(MPR), that is the re-composition technique used mainly, maximum intensity projection (MIP), and volume rendering technique(VRT) and transformation removal from a register modifying VRT, video data were compared and were analyzed by the quantitative method and qualitative method. It is judged as the transformation volume rendering technique of the re-composition technique which is most useful in minimizing the artifact shadow by the exact location of the position of a screw and metal among the analysis and evaluation result computed tomography image reformation technique of the reformation image after the spine screw fixation.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.127-134
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• 2022

In order to overcome the image quality limitations of the conventional C-arm, a flat panel detector (FPD) is used to enhance spatial resolution, detective quantum efficiency, frame rate, and dynamic range. Three-dimensional (3D) visualized information can be obtained from C-arm computed tomography (CT) equipped with an FPD, which can reduce patient discomfort and provide various medical information to health care providers by conducting procedures in the interventional procedure room without moving the patient to the CT scan room. Unlike a conventional C-arm device, a C-arm CT requires different basic safety and essential performance evaluation criteria; therefore, in this study, basic safety and essential performance evaluation criteria to protect patients, medical staff, and radiologists were derived based on International Electrotechnical Commission (IEC) standards, the Ministry of Food and Drug Safety (MFDS) standards in Korea, and the rules on the installation and operation of special medical equipment in Korea. As a result of the study, six basic safety evaluation criteria related to electrical and mechanical radiation safety (leakage current, collision protection, emergency stopping device, overheating, recovery management, and ingress of water or particulate matter into medical electrical (ME) equipment and ME systems: footswitches) and 14 essential performance evaluation criteria (accuracy of tube voltage, accuracy of tube current, accuracy of loading time, accuracy of current time product, reproducibility of radiation output, linearity and consistency in radiography, half layer value in X-ray equipment, focal size and collimator, relationship between X-ray field and image reception area, consistency of light irradiation versus X-ray irradiation, performance of the mechanical device, focal spot to skin distance accuracy, image quality evaluation, and technical characteristic of cone-beam computed tomography) were selected for a total of 20 criteria.

• Imaging Science in Dentistry
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• v.32 no.4
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• pp.195-200
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• 2002

Purpose: To evaluate the computed tomographic appearances of post-operative maxillary sinuses. Materials and Methods: 33 asymptomatic cases of post-operative maxillary sinus without evidence of any pathologic changes and clinical symptoms were selected. CT images were classified as opacification, soft tissue shadow, anterior wall depression, naso-antral communication, and compartmentalization. The relationships between the CT image and the age of patients at the time of operatation, and between the CT image and the duration of time elapsed since the surgical procedure were evaluated. Results: The most commonly presented radiological characteristics that occurred after the Caldwell-Luc procedure were opacification and soft tissue shadow. Anterior wall depression and naso-antral communication were radiographic indications that a Caldwell-Luc operation had been carried out. The age of patients when they had been first operated on, and the duration between the surgical procedure and the time of evaluation had no effect on the CT appearances of normal changes. In cases involving a longer time interval between the antral surgery and evaluation, the anterior wall depression with bony healing was more commonly observed than soft tissue healing. Conclusion: The radiographic information regarding the normal healing state using computed tomography can distinguish post-operative changes from inflammatory and cystic disease in patients who have undergone a Caldwell-Luc type of radical maxillary antrostomy.

• Transactions of Materials Processing
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• v.18 no.5
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• pp.422-427
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• 2009

Computed tomography(CT) has been applied to measure micro-defects in the aluminum knuckle parts manufactured by the thixoforming process. 6061 aluminum alloys were used to form knuckle samples in the semi-solid temperature after the SIMA processing of billets. Tensile specimens were cut from the different locations in a thixoformed knuckle. The size and the distribution of forming defects in tensile specimens were analyzed using CT scanning and image analysis technology before tensile tests. It has been qualitatively shown that the stress-strain curves were significantly affected by the size and the distribution of forming defects although the defect sizes lie in the range of micro-meters.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• The Korean Journal of Nuclear Medicine Technology
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• v.28 no.1
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• pp.35-40
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• 2024

Purpose: To improve the image quality in positron emission tomography (PET), the attenuation correction technique based on the computed tomography (CT) data is important process. However, the artifact is caused by metal material during PET/CT scan, and the image quality is degraded. Therefore, the purpose of this study was to evaluate image quality according to with and without iterative metal artifact reduction (iMAR) algorithm using customized 3D printing phantom. Materials and Methods: The Hoffman and Derenzo phantoms were designed. To protect the gamma ray transmission and express the metal portion, lead substance was located to the surface. The SiPM based PET/CT was used for acquisition of PET images according to application with and without iMAR algorithm. The quantitative methods were used by signal to noise ratio (SNR), coefficient of variation (COV), and contrast to noise ratio (CNR). Results and Discussion: The results shows that the image quality applying iMAR algorithm was higher 1.15, 1.19, and 1.11 times than image quality without iMAR algorithm for SNR, COV, and CNR. Conclusion: In conclusion, the iMAR algorithm was useful for improvement of image quality by reducing the metal artifact lesion.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.21-28
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• 2021

Computed tomography (CT) has the problem of having more radiation exposure compared to other radiographic apparatus. There is a low-dose imaging technique for reducing exposure, but it has a disadvantage of increasing noise in the image. To compensate for this, various noise reduction algorithms have been developed that improve image quality while reducing the exposure dose of patients, of which the median modified Wiener filter (MMWF) algorithm that can be effectively applied to CT devices with excellent time resolution has been presented. The purpose of this study is to optimize the mask size of MMWF algorithm and to see the excellence of noise reduction of MMWF algorithm for existing algorithms. After applying the MMWF algorithm with each mask sizes set from the MASH phantom abdominal images acquired using the MATLAB program, which includes Gaussian noise added, and compared the values of root mean square error (RMSE), peak signal-to-noise ratio (PSNR), coefficient correlation (CC), and universal image quality index (UQI). The results showed that RMSE value was the lowest and PSNR, CC and UQI values were the highest in the 5 x 5 mask size. In addition, comparing Gaussian filter, median filter, Wiener filter, and MMWF with RMSE, PSNR, CC, and UQI by applying the optimized mask size. As a result, the most improved RMSE, PSNR, CC, and UQI values were showed in MMWF algorithms.