• Imaging Science in Dentistry
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• v.51 no.3
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• pp.337-339
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• 2021

• Journal of radiological science and technology
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• v.10 no.1
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• pp.91-104
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• 1987

Computerization for the work of diagnostic radiology department is needed to manage the department efficiently, to deal with the information increasing gradually, and to provide qualified care for patients. There is few computerized management system for diagnostic radiology department in our country. Foreign systems were developed commercially and academically, but almost failed to meet the needs of demands. So in this paper, to help the exploitation of soft ware suitable for the information system of diagnostic radiology department of our country; 1) foreign systems were introduced. 2) Data flow of diagnositc radiology department was analysised by SSA method. 3) Composition of computer system centered on the functions of terminals was presented.

• Imaging Science in Dentistry
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• v.35 no.4
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• pp.185-190
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• 2005

Purpose: Once the decision has been made to adopt an integrated medical information system (IMIS), there are a number of issues to overcome. Users need to be aware of the impact the change will make on end users and be prepared to address issues that arise before they become problems. The purpose of this study is to investigate the benefits and unexpected problems encountered in the implementation of IMIS and to determine a useful framework for IMIS. Materials and Methods: The Yonsei University Dental Hospital is steadily constructing an IMIS. The vendor's PACS software, Piview STAR, supports transactions between workstations that are approved to integrating the healthcare enterprise (IHE) with security function. It is necessary to develop an excellent framework that is good for the patient, healthcare provider and information system vendors, in an expert, efficient, and cost-effective manner. Results : The problems encountered with IMIS implementation were high initial investments, delay of EMR enforcement, underdevelopment of digital radiographic appliances and software and insufficient educational training for users. Conclusions: The clinical environments of dental IMIS is some different from the medical situation. The best way to overcome these differences is to establish a gold standard of dental IMIS integration, which estimates the cost payback. The IHE and its technical framework are good for the patient, the health care provider and all information systems vendors.

• Quality Improvement in Health Care
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• v.9 no.1
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• pp.90-100
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• 2002

Background : This study was attempted to provide us with basic data on how to environmental improvement with patients for examination, and to offer them better treatment. This study was performed to compare the patients, perception between before and after improvement in the diagnostic radiography room. Methods : The data was collected by interviewing 75 patients who underwent the radiography under the diagnostic radiology at Seoul National University Hospital in Korea. The interview ran from August 9 to October 18, 1999. Data were analyzed by percentage and paired t-test. SD(Semantic Differential) method was composed of adjective 13 words. Results : Patients were attending the elementary schools in the Seoul residents. There was no significant difference in kindness unkindness dimension and were significant differences in other dimensions. The mean score of response level to present room was 3.67 and that of improvement room was 2.16. Conclusions : The results of this study suggest a radiography room plan which is considering emotional aspect of children.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2768-2787
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• 2023

As an important research direction of the application of computer science in the medical field, the automatic generation technology of radiology report has attracted wide attention in the academic community. Because the proportion of normal regions in radiology images is much larger than that of abnormal regions, words describing diseases are often masked by other words, resulting in significant feature loss during the calculation process, which affects the quality of generated reports. In addition, the huge difference between visual features and semantic features causes traditional multi-modal fusion method to fail to generate long narrative structures consisting of multiple sentences, which are required for medical reports. To address these challenges, we propose a multi-feature optimization Transformer (MFOT) for generating radiology reports. In detail, a multi-dimensional mapping attention (MDMA) module is designed to encode the visual grid features from different dimensions to reduce the loss of primary features in the encoding process; a feature pre-fusion (FP) module is constructed to enhance the interaction ability between multi-modal features, so as to generate a reasonably structured radiology report; a detail enhanced attention (DEA) module is proposed to enhance the extraction and utilization of key features and reduce the loss of key features. In conclusion, we evaluate the performance of our proposed model against prevailing mainstream models by utilizing widely-recognized radiology report datasets, namely IU X-Ray and MIMIC-CXR. The experimental outcomes demonstrate that our model achieves SOTA performance on both datasets, compared with the base model, the average improvement of six key indicators is 19.9% and 18.0% respectively. These findings substantiate the efficacy of our model in the domain of automated radiology report generation.

• Journal of radiological science and technology
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• v.25 no.1
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• pp.35-37
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• 2002

To assess the new multichannel cochlear implant by radiography in Stenver's projection, because MRI generates artifacts, inducing an electrical current and causing device magnetization. CT is relatively expensive and the metal electrodes scatter the image. Multichannel cochlear implant insertion using the multichannel cochlear implant device. Patients underwent postoperative radiography of their implants. The radiographs were obtained in a Stenver's. The insertion depth of the implant was measured on the radiographs and the results were correlated with the surgical results of insertion depth and with audiometric tests. Patients a correct inserted electrode was found, while in patient complications concerning the electrode were noticed. Radiographs in the Stenver's projection are sufficient for the postoperative assessment of the multichannel cochlear implant device and an exact evaluation of the insertion depth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.576-590
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• 2023

Machine learning is widely used in various academic fields, and recently it has been actively applied in the medical research. In the medical field, machine learning is used in a variety of ways, such as speeding up diagnosis, discovering new biomarkers, or discovering latent traits of a disease. In the respiratory field, a relative regional air volume change (RRAVC) map based on quantitative inspiratory and expiratory computed tomography (CT) imaging can be used as a useful functional imaging biomarker for characterizing regional ventilation. In this study, we seek to predict RRAVC using various regular machine learning models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP). We experimentally show that MLP performs best, followed by XGBoost. We also propose several relative coordinate systems to minimize intersubjective variability. We confirm a significant experimental performance improvement when we apply a subject's relative proportion coordinates over conventional absolute coordinates.

• Journal of the Korean Society of Radiology
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• v.79 no.5
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• pp.247-253
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• 2018

The purpose of this study was to summarize the results of a survey for physicians with specialties other than radiology about imaging studies of patients referred from other institutions. The survey was promoted through individual contacts or social network service and physicians who voluntarily responded to the survey were the subjects of the study. The questionnaire consisted of 11 questions about basic information and referrals about medical imaging. A total of 160 physicians from 30 specialties participated in the survey and 95.6% of the respondents worked in tertiary care center or general hospital. Patients were frequently referred with outside medical images. The most frequently referred imaging modalities were computed tomography and magnetic resonance imaging. However, radiological reports from outside institutions were rarely referred. Most physicians thought that reinterpretation for outside imaging is necessary to acquire a secondary opinion. In conclusion, considering that outside radiological reports are frequently missing and there are high demands on reinterpretation for outside imaging, guidelines for referral of radiological reports with medical imaging, basic elements of radiological reports, and reinterpretation need to be developed.

• Journal of radiological science and technology
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• v.32 no.2
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• pp.195-203
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• 2009

Image registration is a fundamental task in image processing used to match two or more images. It gives new information to the radiologists by matching images from different modalities. The objective of this study is to develop 2D image registration algorithm for PET/CT and CT images acquired by different systems at different times. We matched two CT images first (one from standalone CT and the other from PET/CT) that contain affluent anatomical information. Then, we geometrically transformed PET image according to the results of transformation parameters calculated by the previous step. We have used Affine transform to match the target and reference images. For the similarity measure, mutual information was explored. Use of particle swarm algorithm optimized the performance by finding the best matched parameter set within a reasonable amount of time. The results show good agreements of the images between PET/CT and CT. We expect the proposed algorithm can be used not only for PET/CT and CT image registration but also for different multi-modality imaging systems such as SPECT/CT, MRI/PET and so on.