• Journal of radiological science and technology
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• v.38 no.4
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• pp.483-489
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• 2015

DICOM (Digital Imaging and Communications in Medicine) standards are generally introduced as de facto and de jure standards in modern medical imaging devices to store and to transmit medical image information. DICOM Dose Structured Report (DICOM dose SR) is implemented to report radiation exposure information in image acquiring process. and DIOCM Modality Performed Procedure Step (DICOM MPPS) is also partly used to report this exposure with the information in its DICOM tag. This article is focused on three type of radiation exposure information of DICOM standards, 1) DICOM dose SR, 2) DICOM MPPS and 3) Radiation Exposure Monitoring(REM) profile by Integrating the Healthcare Enterprise(IHE), to study on radiation exposure reporting. Healthcare facility and its staff of medical imaging related to radiation exposure should have a deep understanding of radiation exposure, and it required a standards to enhance the quality control of medical imaging and the safety of patients and staffs. Staff member have to pay attention on radiation exposures and controling processes from the purchasing stage of X-ray devices.

• Korean Journal of Digital Imaging in Medicine
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• v.10 no.1
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• pp.29-34
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• 2008

Purpose To analyze the input and output error of data CD which records the image information and the problems of the server of the compatibility. And to report a compatibility assessment and verification of suitable to DICOM of PACS data CD with investigation of current situation of Korea METHOD AND MATERIALS Date CD of each 8 vendors in 30 hospitals was analyzed. We grasped a main verification element existence of a generation compatibility of data CD. The items of element are media identification, DICOM compression, DICOM viewer send, specified object information modify, auto-run, DICOM content type, etc, and give 1 point for each item. We divided the assessment about an each item into 5 levels. Verification about. DICOM conformance by using DICOM validation tool kit is shown to be classified pass or fail according to error occurrence of tag valus. Classify the prequency of tag occurrence as the item. RESULTS The average point of date CD compatibility is 8 point (very good), lowest is 5 point (6.6%), and highest is 10 point (23%_. Most high occurrence frequency's distribution is 7 point (36.6%). As a result of verification about DICOM conformance, PASS in 8 occurrence frequency's distribution is 7 point (36.6%). As a result of verification about DICOM maximum length numbers (14 items), DICOM error of modality (10 items), discord of pixel data length (6 items). etc.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.108-112
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• 2004

현재 많은 병원의 핵의학과에서 핵의학 장비를 이용하여 많은 수의 핵의학영상을 생성하고 있다. 생성된 핵의학영상은 환자의 질병을 진단 또는 치료하기 위해 기능적 정보를 많이 포함하고 있다. 하지만 이렇게 중요한 기능적 정보가 현재의 PACS 에서는 그 중요한 기능적 정보를 모두 표현하지 못 하는 문제점이 있다. DICOM 에서는 핵의학 영상 및 데이터에 대하여 표준을 정해놓고 그 표준을 따르도록 규정하고 있다. 이러한 DICOM 3.0 표준에서 핵의학 영상 및 데이터에 대하여 표준을 정해놓은 일은 비교적 최근의 일이어서 많은 수의 핵의학 영상 장비나 PACS에서는 핵의학영상에 대한 특징이 반영되지 않고 있는 실정이다. 이에 핵의학 영상의 호환성을 향상과 PACS와 핵의학 장비간의 호환성을 향상시키기 위하여 DICOM 3.0 Part 3에 정의된 IOD 중 꼭 필요하다고 생각되는 최소한의 Tag들을 선별하여 Guideline을 작성하여 DICOM 영상을 Guideline의 내용을 토대로 분석하였고 핵의학 영상이 PACS에서 제대로 활용되지 못 하는 원인을 분석 하였다.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.37-47
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• 2019

The purpose of this paper is to construct a system that matches the patient's image disease information with the medical image viewer in providing the medical image information to the medical staff. Currently, medical image information systems that are commercialized mostly provide only one image viewer with various image information of diseases or use incompatible exclusive viewers. For this reason, we designed and implemented a medical image information viewer matching system that integrates and provides specialized viewers that can be selected by diseases' image information. That is, it is a system to match and view medical image viewers based on disease information extracted from tag information stored as the metadata in DICOM file, which is medical image information standard, for disease-specific viewer matching. We analyzed the execution performances through our retrieval service of medical image information from our implementation system, and showed compatibility and control with various viewers.

• 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.