• Journal of radiological science and technology
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• v.39 no.3
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• pp.329-336
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• 2016

The purpose of this study was to evaluate the usefulness of low-dose CT (LDCT) for emphysema compared with high-resolution CT (HRCT). Measurements of radiation dose and noise were repeated 3 times in same exposure condition which was similar with obtaining HRCT and LDCT images. We analysed reading results of 146 subjects. Six images per participants selected for emphysema grading. Emphysema was graded for all 6 zones on the left and right sides of the lungs by the consensus reading of two chest radiologists using a 4-point scale. Between the HRCT and LDCT images, diagnostic differences and agreements for emphysema were analyzed by McNemar's and unweighted kappa tests, and radiation doses and noise by a Mann-Whitney U-test, using the SPSS 19.0 program. Radiation dose from HRCT was significantly higher than that of LDCT, but the noise was significantly lower in HRCT than in LDCT. Diagnostic agreement for emphysema between HRCT and LDCT images was excellent (k-value=0.88). Emphysema grading scores were not significantly different between HRCT and LDCT images for all six lung zones. Emphysema grading scores from LDCT images were significantly correlated with increased scores on HRCT images (r=0.599, p < 0.001). Considering the tradeoff between radiation dose and image noise, LDCT could be used as the gold standard method instead of HRCT for emphysema detection and grading.

• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.1-9
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• 2022

Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. Therefore, it is very important to predict the prognosis after surgery in patients with non-small cell lung cancer. In this study, the types of preoperative chest CT image patches for non-small cell lung cancer patients with tumor as a region of interest are diversified into five types according to tumor-related information, and performance of single classifier model, ensemble classifier model with soft-voting method, and ensemble classifier model using 3 input channels for combination of three different patches using pre-trained ResNet and EfficientNet CNN networks are analyzed through misclassification cases and Grad-CAM visualization. As a result of the experiment, the ResNet152 single model and the EfficientNet-b7 single model trained on the peritumoral patch showed accuracy of 87.93% and 81.03%, respectively. In addition, ResNet152 ensemble model using the image, peritumoral, and shape-focused intratumoral patches which were placed in each input channels showed stable performance with an accuracy of 87.93%. Also, EfficientNet-b7 ensemble classifier model with soft-voting method using the image and peritumoral patches showed accuracy of 84.48%.

• Journal of Veterinary Clinics
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• v.25 no.3
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• pp.187-191
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• 2008

A 7-year-old, intact female Yorkshire terrier dog was presented for coughing, anorexia, chest pain and dyspnea. Right lateral thoracic radiograph demonstrated a large mass shape on the heart base with decreased cardiac silhouette and severe right deviation of the trachea with the heart shifted to the left thoracic wall was observed on the ventrodorsal thoracic projection. Echocardiographic examination revealed a large rounded mass compressing left atrium around the heart base without signs of pericardial effusion. On computed tomographic (CT) findings, sagittal CT images depicted the possibility of cranial vena caval invasion and heart base involvement of the mass associated with biatrial compression. Dorsal CT image revealed the right deviation of trachea due to the heart base mass and markedly shrunk lung space was detected on the transverse CT image. Because the dog suddenly had died during the recovery from anesthesia after finishing CT scan, necropsy was performed. On gross findings, a large and lobulated mass was located at the base of the heart. A poorly-demarcated, infiltrative, multilobulated tumor composed of polyhedral cells in solid cellular sheets was confirmed based on histopathologic examination. This dog was diagnosed as a chemodectoma. This case report describes the clinical findings, diagnostic consistency of thoracic radiography, echocardiography and CT, and histopathologic confirmation in a spontaneously occurring chemodectoma with a Yorkshire terrier dog.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.591-597
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• 2012

Medical device performance has been advanced while images are expected to be acquired with further higher quality and pertinent applicability as images have been increasing in importance in analyzing major organs. Recent high frequency of image processing by MATLAB in image analysis area accounts for the intent of this study to segment pulmonary vessels by means of MATLAB. This study is to consist of 3 phases including pulmonary region segmentation, pulmonary vessel segmentation and three dimensional connectivity assessment, in which vessel was segmented, using threshold level, from the pulmonary region segmented, vessel thickness was measured as two dimensional refining process and three dimensional connectivity was assessed as three dimensional refining process. It is expected that MATLAB-based image processing should contribute to diversity and reliability of medical image processing and that the study results may lay a foundation for chest CT images-related researches.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.35-43
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• 2009

In the Computer Aided Diagnosis(CAD) System, the efficient way of classifying nodules from chest CT images of a patient is to perform the classification of the remaining part after the pulmonary vessel extraction. During the pulmonary vessel extraction, due to the small difference between the vessel and nodule features in imaging studies such as CT scans after having an injection of contrast, nodule maybe extracted along with the pulmonary vessel. Therefore, the pulmonary vessel extraction method plays an important role in the nodule classification process. In this paper, we propose a nodule reclassification method based on vessel thickness analysis. The proposed method consist of four steps, lung region searching step, vessel extraction and thinning step, vessel topology formation and correction step and the reclassification of nodule in the vessel candidate step. The radiologists helped us to compare the accuracy of the CAD system using the proposed method and the accuracy of general one. Experimental results show that the proposed method can extract pulmonary vessels and reclassify false-positive nodules accurately.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.503-508
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• 2019

In short a binary value according to a change in the tube voltage by using one of VOLUME AXIAL MODE of scanning techniques of chest CT image quality evaluation in order to obtain high image and to present the appropriate tube voltage. CT instruments were GE Revolution (GE Healthcare, Wisconsin USA) model and Phantom used Pediatric Whole Body Phantom PBU-70. The test method was examined in Volume Axial mode using the pediatric protocol used in the Y university hospital of mass-produced material. The tube voltage was set to 70kvp, 80kvp, 100kvp, and mAs was set to smart mA-ODM. The mean SNR difference of the heart was $-4.53{\pm}0.26$ at 70 kvp, $-3.34{\pm}0.18$ at 80 kvp, $-1.87{\pm}0.15$ at 100 kvp, and SNR at 70 kvp was about -2.66 higher than 100 kvp and statistically significant (p<0.05) In the Lung SNR mean difference analysis, $-78.20{\pm}4.16$ at 70 kvp, $-79.10{\pm}4.39$ at 80 kvp, $-77.43{\pm}4.72$ at 100 kvp, and SNR at 70 kvp at about -0.77 higher than 100 kvp were statistically significant. (p<0.05). Lung CNR mean difference was $73.67{\pm}3.95$ at 70 kvp, $75.76{\pm}4.25$ at 80 kvp, $75.57{\pm}4.62$ at 100 kvp and 20.9 CNR at 80 kvp higher than 70 kvp and statistically significant (p<0.05) At 100 kvp of tube voltage, the SNR was close to 1 while maintaining the quality of the heart image when 70 kvp and 80 kvp were compared. However, there is no difference in SNR between 70 kvp and 80 kvp, and 70 kvp can be used to reduce the radiation dose. On the other and, CNR showed an approximate value of 1 at 70 kvp. There is no difference between 80 kvp and 100 kvp. Therefore, 80 kvp can reduce the radiation dose by pediatric chest CT. In addition, it is possible to perform a scan with a short scan time of 0.3 seconds in the volume axial mode test, which is useful for pediatric patients who need to move or relax.

• Journal of radiological science and technology
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• v.31 no.2
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• pp.115-121
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• 2008

Purpose: Aortic Dissection is very dangerous, prognostic disease, which the bloodstream flow out of the true lumen of the aorta by the bursting of aortic intima resulting in a rapid dissociation of inner and outer layer from the media. It is difficult to diagnose aortic dissection clinically by normal X-ray. This study was to investigate the occurrence frequency by age and number of patients who are identified to be aortic dissection by CT (Computed Tomography) scan. Materials and methods: We investigated the trend of yearly fluctuation, gender, age, and department of clinical research of the 112 patients who conducted CT scan in C- University Hospital for two years from January 2005 to December 2006. The MIP and SSD which reconstructed CT image and the VRT image were obtained for the accurate observation. The result was investigated by comparing normal X-ray and CT scan. Results and Conclusion: 1. The yearly check of 112 patients conducted CT scan showed 37 people (41.9%) in 2005, and it was increased to 65 (58.1%) in 2006 by 1.4 times. 2. The gender distribution of patients given a CT scan showed 45 males (40.1%), and female 67 (59.9 %). The aortic dissection patients were 9 (20%) out of 45 males, 21 (31.3%) out of 67 females and women were 1.6 times more than men. Women are also 1.5 times more than men in the number of examinee. 3. The age distribution of patient's who conducted CT scan revealed that there was no patient under 30 years old while 88.3% of all patients were through 41 to 80 years old. The higher the age was, the higher the occurrence of aortic dissection was. The difference in the occurrence frequency of age was statistically significant (p<0.01). 4. The departments that requested CT scan were the emergency department 46 (41.1%), circulatory internal medicine 37 (33.0%), chest surgery 13 (11.6%), and others 6 (14.3%). The combined ratio of emergency medicine and circulatory internal medicine was 74.1% of all. The results show that the aortic dissection is a very dangerous disease whose patients visit mainly via the emergency room. 5. The aortic dissection patients had normal X-ray readings in 22 (73.3%) out of 30, and only 8 (26.7 percent) are abnormal in the X-ray diagnosis. Therefore, the CT scan needs to be enforced in order to assess accurately the disease of aortic dissection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

• Journal of Korean Neurosurgical Society
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• v.38 no.1
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• pp.61-64
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• 2005

We report a 18-year-old man, who has been taking antihypertensive medication for 1month in a local clinic, presented with a sudden onset headache followed by left blindness. He experienced palpitation and chest discomfort during physical exertion since 2years before admission, but unfortunately has been ignored. Brain CT showed intracerebral hemorrhage in the left temporoparietal area, but cerebral angiogram and magnetic resonance image revealed no vascular anomaly. He was managed conservatively, and headache and visual loss were improved over time. Subsequently, on the evaluation of hypertension, he was diagnosed as having extra-adrenal pheochromocytoma on left paraaortic area from the results of endocrinological evaluations, abdominal CT scan, and $^{131}I$-MIBG scintigraphy.