• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.865-872
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• 2023

The purpose of this study was to analyze fabrics suitable for use as examination gowns to determine whether examination gowns affect imaging during anterior to posterior chest examinations(Chest AP) on a digital X-ray system. Examination gowns in use at five medical centers in Seoul were collected and included modal, tencel, cotton, and rayon fabrics. The selection of fabrics was based on studies that reported fabrics with good tactile, absorbent, stretchable, and wrinkle resistance. Phantoms of five hospital gowns and four fabrics, arranged in overlapping layers from one to eight, were created and examined on a digital X-ray system in both Chest AP examination. The images examined were subjected to a first-step profile analysis, a second-step signal intensity averaging analysis, and a third-step microscopic analysis. The results showed that all nine materials had an increasing impact on the image as the number of layers of fabric increased, with the modal fabric having the least impact on the image in the first, second, and third analyses. In conclusion, as the resolution of digital x-ray systems increases, the impact of examination clothing on the image will increase, and research to find suitable materials for examination clothing will continue to be necessary.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.693-704
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• 2024

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AI-based detection assistant tools.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.65-70
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• 2020

PA(postero-anterior) and AP(antero-posterior) chest projections are the most sought-after types of all kinds of projections. But if a radiological technologist puts wrong information about the position in the computer, the orientation of left and right side of an image would be reversed. In order to solve this problem, we utilized CNN(convolutional neural network) which has recently utilized a lot for studies of medical imaging technology and rule-based system. 70% of 111,622 chest images were used for training, 20% of them were used for testing and 10% of them were used for validation set in the CNN experiment. The same amount of images which were used for testing in the CNN experiment were used in rule-based system. Python 3.7 version and Tensorflow r1.14 were utilized for data environment. As a result, rule-based system had 66% accuracy on evaluating whether the orientation reversal on chest x-ray image. But the CNN had 97.9% accuracy on that. Being overcome limitations by CNN which had been shown on rule-based system and shown the high accuracy can be considered as a meaningful result. If some problems which can occur for tasks of the radiological technologist can be separated by utilizing CNN, It can contribute a lot to optimize workflow.

• Journal of Biomedical Engineering Research
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• v.39 no.1
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• pp.48-54
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• 2018

In this study, We developed a Ancillary device for child radiography for X-ray of children under 5 years old and verified its effectiveness. Chest X-rays of children younger than 5 years of age were performed by Supine method at the position of Table detector, Short - Source to Image Receptor Distance(SID). Existing Supine and Short -SID imaging methods cause many problems, such as errors in image reading and excessive radiation exposure dose to patients, but the use of an Ancillary device for child radiography(ADCR) solves these problems. A total of 160 children were divided into the Upright group using ADCR and Supine group without ADCR. The chest X-ray image was visually evaluated by two radiologists with reference to the European Commission's List of Quality Criteria for Diagnostic Radiographic Images in Pediatrics. The total score of the qualitative evaluation was 5.15% higher in the chest upright method using ADCR than in the chest supine method without ADCR, and the chest upright method score was higher than that of the chest supine method in items 1 to 7. whether infants have deep inspiration or not, 4.87% higher for item 1, whether infants rotate or not and the degree of tilting, 0% higher for the item 2, the reproduction of image from just above apices of lungs to T12/L1, 0% for the item 3, reproduction of the vascular pattern in central 2/3 of the lungs, 6.92% higher for the item 4, reproduction of the trachea and the proximal bronchi, 12.9% higher for the item 5, visually sharp reproduction of the diaphragm and costo-phrenic angles, 10% higher for the item 6, reproduction of the spine and paraspinal structures and visualisation of the retrocardiac lung and the mediastinum, and 3.65% higher for the item 7. Items 2 and 3 showed no statistically significant differences(P > 0.05), and items 1, 4, 5, 6, and 7 showed statistically significant differences(P < 0.05). In conclusion, Upright method using ADCR in pediatric chest X-ray is considered as a good alternative to existing Supine method.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.149-158
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• 2022

The pandemic of Covid-19 (Coronavirus Disease 19) has devastated the world, affected millions of people, and disrupted the world economy. The cause of the Covid19 epidemic has been identified as a new variant known as Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV2). It motives irritation of a small air sac referred to as the alveoli. The alveoli make up most of the tissue in the lungs and fill the sac with mucus. Most human beings with Covid19 usually do no longer improve pneumonia. However, chest x-rays of seriously unwell sufferers can be a useful device for medical doctors in diagnosing Covid19-both CT and X-ray exhibit usual patterns of frosted glass (GGO) and consolidation. The introduction of deep getting to know and brand new imaging helps radiologists and medical practitioners discover these unnatural patterns and pick out Covid19-infected chest x-rays. This venture makes use of a new deep studying structure proposed to diagnose Covid19 by the use of chest X-rays. The suggested model in this work aims to predict and forecast the patients at risk and identify the primary COVID-19 risk variables

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.160-169
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• 2021

Background: Dual-energy X-ray images (DEI) can distinguish or improve materials of interest in a two-dimensional radiographic image, by combining two images obtained from separate low and high energies. The concepts of DEI performance describing the performance of double-exposure DEI systems in the Fourier domain been previously introduced, however, the performance of double-exposure DEI itself in terms of various parameters, has not been reported. Materials and Methods: To investigate the DEI performance, signal-difference-to-noise ratio, modulation transfer function, noise power spectrum, and noise equivalent quanta were used. Low- and high-energy were 60 and 130 kVp with 0.01-0.09 mGy, respectively. The energy-separation filter material and its thicknesses were tin (Sn) and 0.0-1.0 mm, respectively. Noise-reduction (NR) filtering used the Gaussian-filter NR, median-filter NR, and anti-correlated NR. Results and Discussion: DEI performance was affected by Sn-filter thickness, weighting factor, and dose allocation. All NR filtering successfully reduced noise, when compared with the dual-energy (DE) images without any NR filtering. Conclusion: The results indicated the significance of investigating, and evaluating suitable DEI performance, for DE images in chest radiography applications. Additionally, all the NR filtering methods were effective at reducing noise in the resultant DE images.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.4
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• pp.195-201
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• 2020

This study studied scoliosis, one of the most common modern diseases caused by lifestyle patterns of office workers sitting in front of computers all day and modern people who use smart phones frequently. Scoliosis is a typical complication that takes more than 80% of the nation's total population at least once. X-ray are used to test for these complications. X-ray, a non-destructive testing method that allows scoliosis to be easily performed and filmed in various areas such as the chest, abdomen and bone without contrast agents or other instruments. We uses NI DAQ to miniaturize digital X-ray imaging devices and image intensifier in self-shielding housing with Vision Assistant for drawing lines to the top and the bottom of the spine to acquire angles, i.e. curvature in real-time. In this way, the research was conducted to see scoliosis patients and their condition easily and to help rapid treatment for solving the problem of posture correction in modern people.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1139-1147
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• 2023

The purpose of this paper is to present and evaluate the performance of a method for controlling the dose for optimal image acquisition while minimizing patient exposure by applying a small-sized Photomultiplier(SiPM) sensor inside a portable detector. Portable detectors have the advantage of being able to quickly access the patient's location for rapid diagnosis, but this mobility comes with the challenge of dose control. This paper presents a method to identify the dose that can have the DQE and optimal image quality of the detector through image evaluation based on IEC62220-1-1, an international standard for X-ray imaging devices, and to identify the optimal dose by matching the ADU of the image and the output of the SiPM Sensor. The Skull AP image was acquired by implementing the detector manufacturer's reference dose. The optimal dose was 342.8 µGy, and the optimal controlled dose was 148.3 µGy, which is 57 % of the manufacturer's reference dose. The Chest AP image was 81.9 µGy and the optimal controlled dose was 27.9 µGy, which is a high dose reduction effect of 66 %. In addition, the two images were analyzed by five radiologists and found to have no clinically significant difference in anatomical delineation.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.595-602
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• 2022

This study tried to suggest the most efficient learning rate for accurate and efficient automatic diagnosis of medical images for chest X-ray pneumonia images using deep learning. After setting the learning rates to 0.1, 0.01, 0.001, and 0.0001 in the Inception V3 deep learning model, respectively, deep learning modeling was performed three times. And the average accuracy and loss function value of verification modeling, and the metric of test modeling were set as performance evaluation indicators, and the performance was compared and evaluated with the average value of three times of the results obtained as a result of performing deep learning modeling. As a result of performance evaluation for deep learning verification modeling performance evaluation and test modeling metric, modeling with a learning rate of 0.001 showed the highest accuracy and excellent performance. For this reason, in this paper, it is recommended to apply a learning rate of 0.001 when classifying the presence or absence of pneumonia on chest X-ray images using a deep learning model. In addition, it was judged that when deep learning modeling through the application of the learning rate presented in this paper could play an auxiliary role in the classification of the presence or absence of pneumonia on chest X-ray images. In the future, if the study of classification for diagnosis and classification of pneumonia using deep learning continues, the contents of this thesis research can be used as basic data, and furthermore, it is expected that it will be helpful in selecting an efficient learning rate in classifying medical images using artificial intelligence.

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.171-176
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• 1987

Wiener filtering method was applied to the dual energy imaging procedure in digital radiography(D.R.). A linear scanning photodiode arrays with 1024 elements(0.6mm H 1.3mm pixel size) were used to obtain chest images in 0.7 sec. For high energy image acquisition, X-ray tube was set at 140KVp, 100mA with a rare-earth phosphor screen. Low energy image was obtained with X-ray tube setting at 70KVp, 150mA. These measured dual energy images are represented in the vector matrix notation as a linear discrete model including the additive random noise. Then, the object images are restored in the minimum mean square error sense using Wiener filtering method in the transformed domain. These restored high and low energy images are used for computation of the basis image decomposition. Then the basis images are linearly combined to produce bone or tissue selective images. Using this process, we could improve the signal to noise ratio characteristics in the material selective images.