• Title/Summary/Keyword: PACS 전송

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Developing Standard Transmission System for Radiology Reporting Including Key Images (Key Image를 포함한 방사선과 판독결과지 표준전송시스템 개발)

  • Kim, Seon-Chil
    • Journal of radiological science and technology
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    • v.30 no.1
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    • pp.47-51
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    • 2007
  • Development of hospital information system and Picture Archiving Communication System is not new in the medical field, and the development of internet and information technology are also universal. In the course of such development, however, it is hard to share medical information without a refined standard format. Especially in the department of radiology, the role of PACS has become very important in interchanging information with other disparate hospital information systems. A specific system needs to be developed that radiological reports are archived into a database efficiently. This includes sharing of medical images. A model is suggested in this study in which an internal system is developed where radiologists store necessary images and transmit them in the standard international clinical format, Clinical Document Architecture, and share the information with hospitals. CDA document generator was made to generate a new file format and separate the existing storage system from the new system. This was to ensure the access to required data in XML documents. The model presented in this study added a process where crucial images in reading are inserted in the CDA radiological report generator. Therefore, this study suggests a storage and transmission model for CDA documents, which is different from the existing DICOM SR. Radiological reports could be better shared, when the application function for inserting images and the analysis of standard clinical terms are completed.

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Design of DICOM Standard Interface Module for Medical Image Standardization (의료영상 표준화를 위한 DICOM 표준 인터페이스 모듈 설계)

  • Kim, Sung-Hyun;Jeon, Jae-Hwan;Kim, Gwan-Hyung;Kang, Sung-In;Oh, Am-Suk
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2010.07a
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    • pp.221-224
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    • 2010
  • PACS(Picture Archiving Communication System)를 바탕으로 하는 DICOM(Digital Imaging and Communications in Medicine)은 주요 의료영상장비들 사이에 데이터와 영상을 효율적으로 교환하고 전송할 수 있도록 마련한 표준안으로 현재 대부분의 최신형 의료영상장비(CT, MR, DSA, CR(Computed Radiology), 초음파검사, 핵의학검사, 내시경검사, 조직병리검사, 등)들은 의료 영상 분야의 국제 표준인 DICOM 표준방식에 의해 영상을 제공하고 있다. 최근 이러한 의료영상장비들은 독립적으로 사용하기보다는 의료수술 모니터링 장비 등의 비 의학영상장비와 서로 연계하여 사용하는 경우가 많아졌다. 그러나 이러한 의료수술 모니터링 장비들은 DICOM 표준 데이터를 고려하지 못하므로 PACS를 통한 데이터 연계에 어려움이 있다. 따라서 본 논문에서는 표준 DICOM 포맷과 의료수술 모니터링 장비의 데이터 구조를 분석하여 non-DICOM 의료수술 모니터링 장비의 PACS 연동을 위한 방안을 제안하였다.

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Design of PACS for the a-D Stereo Endoscopic Images (3차원 스테레오 내시경 영상을 위한 PACS의 설계)

  • Kim, J.H.;Lee, J.Y.;Kim, D.C.;Choi, K.S.;Song, C.G.;Lee, M.H.
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.3236-3237
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    • 2000
  • 본 논문에서는 의료 영상분야에서 많이 활용되고 있는 기존의 영상 획득,저장 및 전송 시스템(PACS)에 스테레오 내시경 영상이 추가될 수 있도록 기존의 PACS에 스테레오 기능의 관찰(viewing) 시스템을 추가하였으며 기본 기능으로 1) 3차원 스테레오 좌,우 독립 영상의 선택과 합성 2) 기존의 합성된 스테레오 영상의 선택이 가능하도록 하였으며 3) 스테레오 영상의 Dicom 표준이 없는 상황을 고려하여 기본적인 카메라 관련 사항(카메라 사양, 초점 거리, 베이스라인 등)을 입력할 수 있는 기록 필드를 삽입하였다. 또한 임상적으로 수용 가능한 3차원 스테레오 영상의 PACS내 효율적 저장법을 제시하기 위하여 의료 영상에 많이 활용되는 JPEG과 Wavelet 압축법을 각각 이용하여 3차원 좌,우 독립영상과 복합 영상의 효율적 압축비를 PSNR을 중심으로 비교하였다.

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Evaluation of Standardized Uptake Value and Metabolic Tumor Volume between Reconstructed data and Re-sliced data in PET Study (PET 검사 시 Reconstructed data와 Re-sliced data의 표준섭취계수와 Metabolic Tumor Volume의 비교 평가)

  • Do, Yong Ho;Lee, Hong Jae;Kim, Jin Eui
    • The Korean Journal of Nuclear Medicine Technology
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    • v.20 no.2
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    • pp.3-8
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    • 2016
  • Purpose SUV is one of the parameters that assist diagnosis in origin, metastasis and staging of cancer. Specially, it is important to compare SUV before and after chemo or radiation therapy to find out effectiveness of treatment. Storing PET data which has no quantitative change is needed for SUV comparison. However, there is a possibility to loss the data in external hard drive or MINIpacs that are managed by department of nuclear medicine. The aim of this study is to evaluate SUV and metabolic tumor volume (MTV) among reconstructed data (R-D) in workstation, R-D and re-sliced data (S-D) in PACS. Materials and Methods Data of 20 patients (aged $60.5{\pm}8.3y$) underwent $^{18}F-FDG$ PET (Biograph truepoint 40, mCT 40, mCT 64, mMR, Siemens) study were analysed. $SUV_{max}$, $SUV_{peak}$ and MTV were measured in liver, aorta and tumor after sending R-D in workstation, R-D and S-D in PACS to syngo.via software. Results R-D of workstation and PACS showed the same value as mean $SUV_{max}$ in liver, aorta and tumor were $2.95{\pm}0.59$, $2.35{\pm}0.61$, $10.36{\pm}6.15$ and $SUV_{peak}$ were $2.70{\pm}0.51$, $2.07{\pm}0.43$, $7.67{\pm}3.73$(p>0.05) respectively. Mean $SUV_{max}$ of S-D in PACS were decreased by 5.18%, 7.22%, 12.11% and $SUV_{peak}$ 2.61%, 3.63%, 10.07%(p<0.05). Correlation between R-D and S-D were $SUV_{max}$ 0.99, 0.96, 0.99 and $SUV_{peak}$ 0.99, 0.99, 0.99. And 2SD in balnd-altman analysis were $SUV_{max}$ 0.125, 0.290, 1.864 and $SUV_{peak}$ 0.053, 0.103, 0.826. MTV of R-D in workstation and PACS show the same value as $14.21{\pm}12.72cm^3$(p>0.05). MTV in PACS was decreased by 0.12% compared to R-D(p>0.05). Correlation and 2SD between R-D and S-D were 0.99 and 2.243. Conclusion $SUV_{max}$, $SUV_{peak}$, MTV showed the same value in both of R-D in workstation and PACS. However, there was statistically difference in $SUV_{max}$, $SUV_{peak}$ of S-D compare to R-D despite of high correlation. It is possible to analyse reliable pre and post SUV if storing R-D in main hospital PACS system.

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A decision support system the interface between PACS and Laboratory Information (PACS 와 임상검사정보의 연동으로 인한 의사결정시스템; 크레아틴 수치정보전송으로 인한 조영제 부작용 예방)

  • Kim, Sean-Chil;Cho, Hune
    • Korean Journal of Digital Imaging in Medicine
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    • v.9 no.1
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    • pp.17-19
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    • 2007
  • This study applies in case of operating an exam using by the contrast order or inputting an order of a contrast media the exam of Radiology Department. It is developed for helping decision making as regards a process of an exam from reading the creatinine value automatically linked with Laboratory Information System. It can be confirmed by real-time information; therefore, the creditability of the information is able to be improved. We will create the base for Patient Monitoring System with the data from the side effect of the creatinine value and allergies. Decision Support System minimize the inconvenience and the riskiness of the given contrast medium for CT tests. We would like to improve medical services by providing a standard circumstance where patients are able to run tests safely and comfortably.

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Real-time Medical And PACS Information System Based on Mobile Networks (모바일 기반 실시간 의료영상정보시스템)

  • Seo, Jeong-Min;Hwang, Jin-Il;Kim, Chi-Hun;Ryu, Kyung-Min
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2011.06a
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    • pp.343-345
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    • 2011
  • 본 논문에서는 기존의 유선을 이용한 의료정보시스템을 현재 최고의 관심사로 떠오르고 있는 스마트폰을 이용한 서비스로 대체하기 위한 프로토타입을 제안한다. 제안하는 시스템은 무선 네트워크기반의 전송시스템과 기존의 HIS(Hospital Information System)와 연계하여 병원의 의학영상, 관련 임상정보, ADT(입 퇴원, 전과)를 시스템화하여 효율적으로 통합 관리할 수 있다.

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A Study on the Extension of Picture Archiving and Communication System (의료영상저장전송 시스템(PACS)의 확장에 관한 연구)

  • Seen Dong-June;Hwang Suk-Hyung;Choi Sung-Hee
    • Proceedings of the Korea Information Processing Society Conference
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    • 2004.11a
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    • pp.595-598
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    • 2004
  • 현재 급속하게 이루어지고 있는 의료영상저장전송 시스템의 보급으로 컴퓨터를 이용한 의료영상분야의 발전이 가속도를 받고 있다. 그러나 업체마다 통신관련 프로토콜 적용에 다소 차이가 존재하기 때문에 이후 도입해야 하는 CAD(Computer Aided Diagnosis) 등 분야로의 확장에 문제가 있다. 본 연구에서는 의료영상저장전송 시스템을 확장하고자 하는 경우에 고려해야 할 사항들에 대해서 제안하고 이를 토대로 새로운 의료영상저장전송 시스템을 구축하였다.

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Compression of Medical Images Using DWT (DWT을 이용한 의료영상 압축)

  • Lim, Jae-Dong;Lee, Sang-Bock
    • Journal of the Korean Society of Radiology
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    • v.2 no.2
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    • pp.11-16
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    • 2008
  • The most difficult of implementation PACS is large amount of data. Therefore, PACS needs mass storage, as well as rapid transmission time. Consequently, medical images needs compression when stored in PACS. WT(wavelet transform) was announced by Ingrid Daubechies and Stephane Mallat, WT was methods of signal analysis by a base functions set same as Fourie transform. This paper estimated an efficiency, that experimental medical images compressed by DWT. The result of estimated, we are knows effectiveness that display to remained signal in low frequency region after 4-level DWT form $512{\times}512{\times}2^8$ input images. Compression ratio of images by 4-level DWT was 1:16. It is a high compression ratio, the other side has a problem appears on staircase phenomenon.

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Design and Implementation of Hospital Information Exchange System using XML Document (XML 문서를 이용한 환자 정보 교환 시스템(HIES)의 설계 및 구현)

  • 홍동완
    • Proceedings of the Korean Information Science Society Conference
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    • 2000.10a
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    • pp.234-236
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    • 2000
  • 최근 국내의 모든 병원에서 PACS(Picture Atchiving and Communication System) 도입에 관한 관심을 보이고 있다. PACS가 구축이 되면 병원 내 모든 진료 과에서 디지털 데이터의 전송으로 정보를 공유할 수 있고, 진료가 자동화 되는 장점이 있다. 하지만, 환자가 다른 병원으로 이송될 경우 과거 진료 내역을 다른 병원으로 함께 전송하여야 되는데, 다른 병원의 시스템과 연계할 방법이 현재로는 존재하지 않는다. HIES 시스템은 의료 데이터 전송의 표준문서로 XML(eXtensible Markup Language)을 제안하고 있다. XML은 문서를 정의하는 메타 마크업(meta-markup) 언어로써 DICOM 프로토콜을 통하여 산출된 의료 데이터를 표현하기에 적당하다. 또한 병원 간 이질 데이터베이스 시스템 통합을 위하여 일관된 스키마 정보를 유지하는 정보 공유 관리자를 설계, 구현하였다.

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A Study on the Development Direction of Medical Image Information System Using Big Data and AI (빅데이터와 AI를 활용한 의료영상 정보 시스템 발전 방향에 대한 연구)

  • Yoo, Se Jong;Han, Seong Soo;Jeon, Mi-Hyang;Han, Man Seok
    • KIPS Transactions on Computer and Communication Systems
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    • v.11 no.9
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    • pp.317-322
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    • 2022
  • The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.