• Imaging Science in Dentistry
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• v.32 no.2
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• pp.107-111
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• 2002

Purpose : To compare the accuracy of digital subtraction images acquired by two different methods different in positioning four reference points for geometrical standardization. Materials and Methods : A total of 36 digital radiographic images of 6 volunteers were taken at the areas of the incisor, premolar, and molar of both the maxilla and mandible using the Digora system. Each image was moved 4 mm vertically and horizontally. Four oral and maxillofacial radiologists performed digital subtraction radiography between the paired images before and after movement using Emago (Oral Diagnostic Systems, Amsterdam, The Netherlands) and Sunny (Biomedisys Co., Seoul, Korea). The standard deviation of the internal gray value in Region of Interest (ROI) was statistically analyzed between the two programs using the paired t-test. Results : The standard deviation of pixel gray values from the digital subtraction images using the Sunny program were lower than that of the Emago program (p<0.05). All observers showed significant differences between each other when the Sunny program was used (p<0.05), but one observer showed a significantly higher score than other observers when they used Emago (p<0.05). The standard deviations of premolar area from both Sunny and Emago programs were significantly higher than those of anterior and molar regions (p<0.05). Conclusion: The subtracted images using the Sunny program were more accurate and sensitive than those taken using the Emago program.

• Journal of radiological science and technology
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• v.26 no.3
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• pp.7-11
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• 2003

Currently, many hospitals are hastening to introduce digital radiography systems. This is a direct result of the intentions to improve medical services and to digitalize radiology information systems, and is also leading to the improvement of medical imaging technology. Throughout F/S system's long history, many people have researched the image quality and dosage concerning these systems, and as a result, huge improvements in the dosage of patients were possible. Similarly, I believe that DR systems need the same kind of effort. Of course, decreases in dosage that ignore image quality are unthinkable. The results of experiments conducted by five hospitals during a period of 3 months brought to us the conclusions listed below. 1. Based on the comparison and analysis of the exposure control of F/S systems and DR systems, DR systems generally showed higher exposure control for parts of the phantom that became thicker, and the exposure control improved rapidly as the thickness increased. 2. DR systems still proved to be somewhat deficient in resolution measurements compared to existing F/S systems. The image processing part of DR systems contributed much to these result. 3. Under conditions used clinically, the dosage measurements of DR systems were generally higher regardless of region. 4. According to the evaluation of image quality, DR systems showed a higher degree of satisfaction as the thickness of the region became thinner. As mentioned above and based on the mutual relationship experiments between the dosage and image quality of F/S systems and DR systems, research to increase the satisfaction of DR systems must be considered.

• Progress in Medical Physics
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• v.16 no.1
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• pp.47-51
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• 2005

We report a case of an accidental extravasation of contrast material. A large-volume extravasation occurred in an adult during spiral contrast-enhanced CT. The amount of contrast material extravasated was 47 ml. The patient had a swelling of the dorsum right hand. The extravasation injury site was determined by CT scanning. The extavasation case was examined using five separate display techniques: axial, multi planar reformation (MPR), maximum intensity projection (MIP), volume rendering, and shaded-surfaced display (SSD). This paper introduces extravasation with the CT and the three-dimensional appearance.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.239-246
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• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

• Korean Journal of Digital Imaging in Medicine
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• v.7 no.1
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• pp.33-38
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• 2005

Modern hospitals have been greatly facilitated with information technology (IT) such as hospital information system (HIS). One of the most prominent achievements is medical imaging and image data management so-called Picture Archiving and Communication Systems (PACS). Due to inevitable use of diagnostic images (such as X-ray, CT, MRI), PACS made tremendous impact not only on radiology department but also nearly all clinical departments for exchange and sharing image related clinical information. There is no doubt that better use of PACS leads to highly efficient clinical administration and hospital management. However, due to rapid and widespread acceptance of PACS storage and management of digitized image data in hospital introduces overhead and bottleneck when transferring images among clinical departments within and/or across hospitals. Despite numerous technical difficulties, financing for installing PACS is a major hindrance to overcome. In addition, a mirroring or a clustering backup can be used to maximize security and efficiency, which may not be considered as cost-effective approach because of extra hardware expenses. In this study therefore we have developed a new based on grid of distributed PACS in order to balance between the cost and network performance among multiple hospitals.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.395-396
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• 2007

Bone age assessment has been widely used to measure the ossification in pediatric radiology. For the assessment, first bone age of each epiphyseal plate is estimated using DCT/LDA, then the bone age of a patient is calculated by using the median of 9 estimations. For some patients, however, due to various reasons such as X-ray image quality or the pose of fingers, it is common to miss couple of plates in automated systems. In this paper, we investigate the relationship between the number of detected plates and the accuracy of bone age assessment. In the experimental results, we confirmed the similarity between bone age assessed using more than 7 epiphyseal plates and that assessed using 9 epiphyseal plates.

• Progress in Medical Physics
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• v.17 no.1
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• pp.32-39
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• 2006

To assess the ability of an extravasation detection accessory (EDA) system to detect clinically important extravascular injection of iodinated contrast material delivered with an automated power injector. Fifty patients referred for contrast material enhanced body computed tomography studied in a prospective, observation study in which the EDA system was used to identify and interrupt any injection associated with clinically Important extravasation. The presence or absence of extravasation was definitively established with multi-detector CT at the injection site (injection rate, $2.0{\sim}2.5$ mL/sec). There were two true positive, extravasation volumes $22{\sim}25$ mL. The EDA system had sensitivity in the detection of clinically important extravasation. The EDA system is easy to use, safe, and accurate In the monitoring of intravenous injections for extravasation, which may prove especially useful in CT applications.

• Journal of radiological science and technology
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• v.24 no.2
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• pp.65-70
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• 2001

The purpose of this study is to prevent nosocomial infection in patients through contact of radiographic cassettes. Data were collected from radiographers working in 29 university hospitals in Seoul in February and March 2001. Radiographic cassettes were disinfected daily in 5 hospitals, weekly in 4 hospitals, monthly in 5 hospitals, bimonthly in 1 hospital and once every three months in another hospital. 12 other hospitals do not practice regular disinfections of radiographic cassettes. Gauze soaked in disinfectant solution is used in 7 hospitals while 11 hospitals used cotton and cloth soaked in disinfectant solution to clean the radiographic cassettes. 26 hospitals used 99% alcohol based disinfectant solutions while 3 hospitals used 75% alcohol based disinfectant. 26 hospitals use of intercourse cassettes outpatients and in patients. In 26 hospitals, all patients shared the same set of radiographic cassettes used in the hospitals, or in 26 hospitals, separate sets of radiographic cassettes are used for outpatients and inpatients. Separate sets of cassettes are used for ICU and inpatients in 6 others hospitals. 23 hospitals used the same sets of radiographic cassettes for all their patients. radiographic cassettes are cleaned in wash area in the study room of the radiographic department in 17 hospitals. 12 other hospitals do not have designated cleaning areas for the cassettes. All radiographers practiced hands washing with soap. All 29 hospitals surveyed have infection control committee. However, only 9 out of the 29 hospitals surveyed provided Infection disinfections control education to radiographers. Only 3 hospitals have radiographers sitting in the infection control committee. Infection management education is conducted in 63 hospitals annually, twice a year in 1 hospital and once every 3 months in 2 hospitals.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• Journal of the Korean Society of Radiology
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• v.13 no.5
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• pp.757-763
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• 2019

Recently, Following the recent development of flat panel detector with wide dynamic ranges, increasing numbers of healthcare providers have begun to use digital radiography. As a result, filter thickness standards should be reestablished, as current clinical practice requires the use of thicknesses recommended by the National Council on Radiation Protection and Measurements, which are based on information, acquired using conventional analog systems. Here we investigated the possibility of minimizing dose creep and optimizing patient dose using Al filters in digital radiography. The use of thicker Al filters resulted in a maximum 19.3% reduction in the entrance skin exposure dose when medical images with similar sharpness values were compared. However, resolution, which is a critical factor in imaging, had a significant change of 1.01 lp/mm. This change in resolution is thought to be due to the increased amount of scattered rays generated from the object due to the X-ray beam hardening effect. The increase in the number of scattered rays was verified using the scattering degradation factor. However, the FPD, which has recently been developed and is widely used in various areas, has greater response to radiation than analog devices and has a wide dynamic range. Therefore, the FPD is expected to maintain an appropriate level of resolution corresponding to the increase in the scattered-ray content ratio, which depends on filter thickness. Use of the FPD is also expected to minimize dose creep by reducing the exposure dose.