Digital Imaging And Communications In Medicine Standard Research Articles

Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiograph (CXR) images. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images. Using 2,321 CXRs from 897 patients (median age, 76 years (range 18-96 years); 625 male, 272 female), CIEDs were categorized into four manufacturers, 27 models, and one 'other' category. Five smartphones were used to acquire 11,072 images. Performance was reported using the Dice coefficient on the validation set for segmentation or balanced accuracy on the test set for manufacturer and model classification, respectively. Results The segmentation tool achieved a mean Dice coefficient of 0.936 (IQR: 0.890-0.958). The model had an accuracy of 94.36% (95% CI: 90.93%-96.84%; n = 251/266) for CIED manufacturer classification and 84.21% (95% CI: 79.31%-88.30%; n = 224/266) for CIED model classification. Conclusion The proposed deep learning model, trained on both traditional DICOM and smartphone images, showed high accuracy for segmentation and classification of CIEDs on CXRs. ©RSNA, 2024.

Thirty Years of the DICOM Standard.

Digital Imaging and Communications in Medicine (DICOM) is an international standard that defines a format for storing medical images and a protocol to enable and facilitate data communication among medical imaging systems. The DICOM standard has been instrumental in transforming the medical imaging world over the last three decades. Its adoption has been a significant experience for manufacturers, healthcare users, and research scientists. In this review, thirty years after introducing the standard, we discuss the innovation, advantages, and limitations of adopting the DICOM and its possible future directions.

Analysis of pulmonary artery variation based on 3D reconstruction of CT angiography.

Objective: The aim of this study is to acquire pulmonary CT (Computed tomography) angiographic data for the purpose of creating a three-dimensional reconstruction. Additionally, we aim to analyze the features and deviations of the branches in both pulmonary lobes. This information is intended to serve as a more comprehensive and detailed reference for medical professionals when conducting preoperative evaluations and devising surgical plans. Method: Between August 2019 and December 2021, 420 patients were selected from the thoracic surgery department at the First Hospital of Jilin University, and underwent pulmonary 64 channel contrast enhanced CT examinations (Philips ICT 256). The images were acquired at a 1.5mm slice thickness, and the DCM files that complied with DICOM (Digital Imaging and Communications in Medicine) standards were analysed for 3D (three dimensional) reconstruction using Mimics 22.0 software. The reconstructed pulmonary artery models were assessed by attending chest surgeons and radiologists with over 10years of clinical experience. The two-dimensional image planes, as well as the coronary and sagittal planes, were utilized to evaluate the arteries. The study analyzed the characteristics and variations of the branches and courses of pulmonary arteries in each lobe of the lungs, with the exception of the subsegmental arterial system. Two chest surgeons and two radiologists with professional titles-all of whom had over a decade of clinical experience-jointly evaluated the 3D models of the pulmonary artery and similarly assessed the characteristics and variations of the branches and courses in each lobe of the lungs. Results: Significant variations were observed in the left superior pulmonary artery across the 420 subjects studied. In the left upper lobe, the blood supply of 4 arteries accounted for 50.5% (n = 212), while the blood supply of 2 arteries in the left lower lobe was the most common, accounting for 79.5% (n = 334). The greatest variation in the right pulmonary artery was observed in the branch supply of the right upper lobe mediastinal artery. In the majority of cases (77.9%), there were two arteries present, which was the most common configuration observed accounting for 64% (n = 269). In the right inferior lobe of the lung, there were typically 2-4 arteries, with 2 arteries being the most common configuration (observed in 79% of cases, n = 332). Conclusion: The three-dimensional reconstruction of pulmonary artery CT angiography enables clear observation of the branches and distribution of the pulmonary artery while also highlighting any variations. This technique holds significant clinical value for preoperative assessments regarding lesions and blood vessels.

Clinical Applications of Mixed Reality and 3D Printing in Congenital Heart Disease.

Understanding the anatomical features and generation of realistic three-dimensional (3D) visualization of congenital heart disease (CHD) is always challenging due to the complexity and wide spectrum of CHD. Emerging technologies, including 3D printing and mixed reality (MR), have the potential to overcome these limitations based on 2D and 3D reconstructions of the standard DICOM (Digital Imaging and Communications in Medicine) images. However, very little research has been conducted with regard to the clinical value of these two novel technologies in CHD. This study aims to investigate the usefulness and clinical value of MR and 3D printing in assisting diagnosis, medical education, pre-operative planning, and intraoperative guidance of CHD surgeries through evaluations from a group of cardiac specialists and physicians. Two cardiac computed tomography angiography scans that demonstrate CHD of different complexities (atrial septal defect and double outlet right ventricle) were selected and converted into 3D-printed heart models (3DPHM) and MR models. Thirty-four cardiac specialists and physicians were recruited. The results showed that the MR models were ranked as the best modality amongst the three, and were significantly better than DICOM images in demonstrating complex CHD lesions (mean difference (MD) = 0.76, p = 0.01), in enhancing depth perception (MD = 1.09, p = 0.00), in portraying spatial relationship between cardiac structures (MD = 1.15, p = 0.00), as a learning tool of the pathology (MD = 0.91, p = 0.00), and in facilitating pre-operative planning (MD = 0.87, p = 0.02). The 3DPHM were ranked as the best modality and significantly better than DICOM images in facilitating communication with patients (MD = 0.99, p = 0.00). In conclusion, both MR models and 3DPHM have their own strengths in different aspects, and they are superior to standard DICOM images in the visualization and management of CHD.

The Role of DICOM in Artificial Intelligence for Skin Disease.

There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology. Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging. Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging. DICOM is a continually evolving standard. There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard. The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (e.g., image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology. DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts. DICOM can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation. This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols. DICOM can address several important technological and workflow challenges for the implementation of AI. However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology.

Design and Implementation of the Pre-Clinical DICOM Standard in Multi-Cohort Murine Studies.

The small animal imaging Digital Imaging and Communications in Medicine (DICOM) acquisition context structured report (SR) was developed to incorporate pre-clinical data in an established DICOM format for rapid queries and comparison of clinical and non-clinical datasets. Established terminologies (i.e., anesthesia, mouse model nomenclature, veterinary definitions, NCI Metathesaurus) were utilized to assist in defining terms implemented in pre-clinical imaging and new codes were added to integrate the specific small animal procedures and handling processes, such as housing, biosafety level, and pre-imaging rodent preparation. In addition to the standard DICOM fields, the small animal SR includes fields specific to small animal imaging such as tumor graft (i.e., melanoma), tissue of origin, mouse strain, and exogenous material, including the date and site of injection. Additionally, the mapping and harmonization developed by the Mouse-Human Anatomy Project were implemented to assist co-clinical research by providing cross-reference human-to-mouse anatomies. Furthermore, since small animal imaging performs multi-mouse imaging for high throughput, and queries for co-clinical research requires a one-to-one relation, an imaging splitting routine was developed, new Unique Identifiers (UID’s) were created, and the original patient name and ID were saved for reference to the original dataset. We report the implementation of the small animal SR using MRI datasets (as an example) of patient-derived xenograft mouse models and uploaded to The Cancer Imaging Archive (TCIA) for public dissemination, and also implemented this on PET/CT datasets. The small animal SR enhancement provides researchers the ability to query any DICOM modality pre-clinical and clinical datasets using standard vocabularies and enhances co-clinical studies.

DICOM’s Standardization in Histo-Pathology

Background: The Digital Imaging and Communications in Medicine (DICOM) standard helps to represent, store, and to exchange healthcare images associated with its data. DICOM develops over time and is continuously adapted to match the rigors of new clinical demands and technologies. An uphill battle in this regard is to conciliate new software programs with legacy systems.
 Methods: This work discusses the essential aspects of the standard and assesses its capabilities and limitations in a multisite, multivendor healthcare system aiming at Whole Slicing Image (WSI) procedures. Selected relevant DICOM attributes help to develop and organize WSI applications that extract and handle image data, integrated patient records, and metadata. DICOM must also interface with proprietary file formats, clinical metadata and from different laboratory information systems. Standard DICOM validation tools to measure encoding, storing, querying and retrieval of medical data can verify the generated DICOM files over the web.
 Results: This work investigates the current regulations and recommendations for the use of DICOM with WSI data. They rely mostly on the EU guidelines that help envision future needs and extensions based on new examination modalities like concurrent use of WSI with in-vitro imaging and 3D WSI.
 Conclusion: A DICOM file format and communication protocol for pathology has been defined. However, adoption by vendors and in the field is pending. DICOM allows efficient access and prompt availability of WSI data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology. In the future, the DICOM standard will have to address several issues due to the way samples are gathered and encompassing new imaging technologies.

Development of an age-scalable 3D computational phantom in DICOM standard for late effects studies of childhood cancer survivors

Purpose: We previously developed an age-scalable 3D computational phantom that has been widely used for retrospective whole-body dose reconstructions of conventional two-dimensional historic radiation therapy (RT) treatments in late effects studies of childhood cancer survivors. This phantom is modeled in the FORTRAN programming language and is not readily applicable for dose reconstructions for survivors treated with contemporary RT whose treatment plans were designed using computed tomography images and complex treatment fields. The goal of this work was to adapt the current FORTRAN model of our age-scalable computational phantom into Digital Imaging and Communications in Medicine (DICOM) standard so that it can be used with any treatment planning system (TPS) to reconstruct contemporary RT. Additionally, we report a detailed description of the phantom’s age-based scaling functions, information that was not previously published. Method: We developed a Python script that adapts our phantom model from FORTRAN to DICOM. To validate the conversion, we compared geometric parameters for the phantom modeled in FORTRAN and DICOM scaled to ages 1 month, 6 months, 1, 2, 3, 5, 8, 10, 15, and 18 years. Specifically, we calculated the percent differences between the corner points and volume of each body region and the normalized mean square distance (NMSD) between each of the organs. In addition, we also calculated the percent difference between the heights of our DICOM age-scaled phantom and the heights (50th percentile) reported by the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) for male and female children of the same ages. Additionally, we calculated the difference between the organ masses for our DICOM phantom and the organ masses for two reference phantoms (from International Comission on Radiation Protection (ICRP) 89 and the University of Florida/National Cancer Institute reference hybrid voxel phantoms) for ages newborn, 1, 5, 10, 15 and adult. Lastly, we conducted a feasibility study using our DICOM phantom for organ dose calculations in a commercial TPS. Specifically, we simulated a 6 MV photon right-sided flank field RT plan for our DICOM phantom scaled to age 3.9 years; treatment field parameters and age were typical of a Wilms tumor RT treatment in the Childhood Cancer Survivor Study. For comparison, the same treatment was simulated using our in-house dose calculation system with our FORTRAN phantom. The percent differences (between FORTRAN and DICOM) in mean dose and percent of volume receiving dose ≥5 Gy were calculated for two organs at risk, liver and pancreas. Results: The percent differences in corner points and the volumes of head, neck, and trunk body regions between our phantom modeled in FORTRAN and DICOM agreed within 3%. For all of the ages, the NMSDs were negliglible with a maximum NMSD of mm for occiptital lobe of 1 month. The heights of our age-scaled phantom agreed with WHO/CDC data within 7% from infant to adult, and within 2% agreement for ages 5 years and older. We observed that organ masses in our phantom are less than the organ masses for other reference phantoms. Dose calculations done with our in-house calculation system (with FORTRAN phantom) and commercial TPS (with DICOM phantom) agreed within 7%. Conclusion: We successfully adapted our phantom model from the FORTRAN language to DICOM standard and validated its geometric consistency. We also demonstrated that our phantom model is representative of population height data for infant to adult, but that the organ masses are smaller than in other reference phantoms and need further refinement. Our age-scalable computational phantom modeled in DICOM standard can be scaled to any age at RT and used within a commercial TPS to retrospectively reconstruct doses from contemporary RT in childhood cancer survivors.

Dual-Personality DICOM-TIFF for Whole Slide Images: A Migration Technique for Legacy Software

Despite recently organized Digital Imaging and Communications in Medicine (DICOM) testing and demonstration events involving numerous participating vendors, it is still the case that scanner manufacturers, software developers, and users continue to depend on proprietary file formats rather than adopting the standard DICOM whole slide microscopic image object. Many proprietary formats are Tag Image File Format (TIFF) based, and existing applications and libraries can read tiled TIFF files. The sluggish adoption of DICOM for whole slide image encoding can be temporarily mitigated by the use of dual-personality DICOM-TIFF files. These are compatible with the installed base of TIFF-based software, as well as newer DICOM-based software. The DICOM file format was deliberately designed to support this dual-personality capability for such transitional situations, although it is rarely used. Furthermore, existing TIFF files can be converted into dual-personality DICOM-TIFF without changing the pixel data. This paper demonstrates the feasibility of extending the dual-personality concept to multiframe-tiled pyramidal whole slide images and explores the issues encountered. Open source code and sample converted images are provided for testing.

Radiomics Prediction of Radiation Treatment Outcomes in Oropharyngeal Cancer: A Clinical and Image Repository in Concert with the Cancer Imaging Archive (TCIA)

There is an unmet need for integrating quantitative imaging biomarkers into current risk stratification tools. To explore the correlation between radiomics features – alone or in combination with clinical prognosticators- and tumor outcome, we retrieved clinical meta-data and matched baseline contrast-enhanced computed tomography (CECT) scans from a single institution, institutional review board-approved cohort of 495 oropharyngeal cancer (OPC) patients. We opted to publicly share this large curated data set and subsequent radiomics analytical outcome via The Cancer Imaging Archive (TCIA) to serve as a resource for optimized standardization in the radiomics field. Diagnostic CECT images were acquired at our institution between 2005 and 2012 for 495 OPC patients (prior to any active intervention) in Digital Imaging and Communications in Medicine (DICOM) format . Expert radiation oncologists manually segmented primary and nodal disease gross volumes (GTVp & GTVn). Structure sets were named per the American Association of Physicists in Medicine (AAPM) TG-263 recommendations, then retrieved in DICOM RTSTRUCT format. Matched patient, disease, treatment and outcomes data were obtained. Radiomics analysis was performed using an open-source institutionally-developed software that runs on a computer algorithm. Protected health information (PHI) was removed from all DICOM elements in compliance with the DICOM standards committee P.S. 3.15 Annex E, Attribute Confidentiality Profile using the Radiological Society of North America clinical trial processor (RSNA CTP). TCIA’s curation process ensures the DICOM data is free of PHI and all linkages between CT and structure sets are correct. Following curation the data was archived in TCIA as a permanent open-access collection named “HNSCC”. Anonymized data for 495 OPC patients will be made publicly available from TCIA as downloadable DICOM files (N=**), clinical data, and processed outputs, i.e. tumor segmentations and extracted radiomics features. All data attributes can be cross-referenced via the same anonymized subject IDs. We prepared a data dictionary that specifies clinical data attributes, definitions and possible variables per North American Association of Central Cancer Registries (NAACCR) guidelines. Large-scale data curation-anonymization-transfer workflows, as well as advanced image registration algorithms and common ontology data dictionaries, are unmet needs for joint machine-learning/radiomics research projects. If these resources are paired with large, curated data sets, like those provided via open-access mega-data repositories, like TCIA, the potential benefits include identification of imaging-derived radiomics signatures associated with treatment outcomes and normal-tissue complications across various head and neck epidemiologic cohorts.

Specifications and Standards for Insect 3D Data

With the continuous development of imaging technology, the amount of insect 3D data is increasing, but research on data management is still virtually non-existent. This paper will discuss the specifications and standards relevant to the process of insect 3D data acquisition, processing and analysis. The collection of 3D data of insects includes specimen collection, sample preparation, image scanning specifications and 3D model specification. The specimen collection information uses existing biodiversity information standards such as Darwin Core. However, the 3D scanning process contains unique specifications for specimen preparation, depending on the scanning equipment, to achieve the best imaging results. Data processing of 3D images includes 3D reconstruction, tagging morphological structures (such as muscle and skeleton), and 3D model building. There are different algorithms in the 3D reconstruction process, but the processing results generally follow DICOM (Digital Imaging and Communications in Medicine) standards. There is no available standard for marking morphological structures, because this process is currently executed by individual researchers who create operational specifications according to their own needs. 3D models have specific file specifications, such as object files (https://en.wikipedia.org/wiki/Wavefront_.obj_file) and 3D max format (https://en.wikipedia.org/wiki/.3ds), which are widely used at present. There are only some simple tools for analysis of three-dimensional data and there are no specific standards or specifications in Audubon Core (https://terms.tdwg.org/wiki/Audubon_Core), the TDWG standard for biodiversity-related multi-media. There are very few 3D databases of animals at this time. Most of insect 3D data are created by individual entomologists and are not even stored in databases. Specifications for the management of insect 3D data need to be established step-by-step. Based on our attempt to construct a database of 3D insect data, we preliminarily discuss the necessary specifications.

Dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM.

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi Cancer Res; 77(21); e87-90. ©2017 AACR.

CONTENT PRESERVING WATERMARKING FOR MEDICAL IMAGES USING SHEARLET TRANSFORM AND SVD

Abstract. Medical Image Watermarking (MIW) is a special field of a watermarking due to the requirements of the Digital Imaging and COmmunications in Medicine (DICOM) standard since 1993. All 20 parts of the DICOM standard are revised periodically. The main idea of the MIW is to embed various types of information including the doctor’s digital signature, fragile watermark, electronic patient record, and main watermark in a view of region of interest for the doctor into the host medical image. These four types of information are represented in different forms; some of them are encrypted according to the DICOM requirements. However, all types of information ought to be resulted into the generalized binary stream for embedding. The generalized binary stream may have a huge volume. Therefore, not all watermarking methods can be applied successfully. Recently, the digital shearlet transform had been introduced as a rigorous mathematical framework for the geometric representation of multi-dimensional data. Some modifications of the shearlet transform, particularly the non-subsampled shearlet transform, can be associated to a multi-resolution analysis that provides a fully shift-invariant, multi-scale, and multi-directional expansion. During experiments, a quality of the extracted watermarks under the JPEG compression and typical internet attacks was estimated using several metrics, including the peak signal to noise ratio, structural similarity index measure, and bit error rate.

Real-Time Patient Radiation Dose Monitoring System Used in a Large University Hospital.

Radiation dose monitoring in medical imaging examination areas is mandatory for the reduction of patient radiation exposure. Recently, dose monitoring techniques that use digital imaging and communications in medicine (DICOM) dose structured reports (SR) have been introduced. The present paper discusses the setup of a radiation dose monitoring system based on DICOM data from university hospitals in Korea. This system utilizes the radiation dose data-archiving method of standard DICOM dose SR combined with a DICOM modality performed procedure step (MPPS). The analysis of dose data based on a method utilizing DICOM tag information is proposed herein. This method supports the display of dose data from non-dosimeter-attached X-ray equipment. This system tracks data from 62 pieces of equipment to analyze digital radiographic, mammographic, mobile radiographic, CT, PET-CT, angiographic, and fluorographic modalities.

Storage of Fractional Flow Reserve Hemodynamic Waveforms Using Semantic Extension of the DICOM Standard.

Visual assessment of coronary stenoses by coronary angiography remains widely used but correlates poorly with ischemia, particularly for moderate lesions. Fractional flow reserve (FFR) is a cardiac catheterization procedure that aims to provide objective measures of coronary lesion hemodynamic significance and involves the acquisition of phasic pressure and electrocardiographic waveforms. The dataset from these procedures currently remains in proprietary systems with restricted data access, inability for data exchange, and often inadequate archiving. Digital Imaging and Communications in Medicine (DICOM) includes a waveform information object definition. We describe the method of encapsulating FFR procedural information into a DICOM waveform file. We define private data elements to capture modality-specific data that is not represented by standard DICOM data elements. We propose the adoption of this semantic extension of the DICOM waveform information object for exchange and archiving of data from studies of pressure-derived indices of coronary stenoses.

Technology and Technique Standards for Camera-Acquired Digital Dermatologic Images: A Systematic Review.

Photographs are invaluable dermatologic diagnostic, management, research, teaching, and documentation tools. Digital Imaging and Communications in Medicine (DICOM) standards exist for many types of digital medical images, but there are no DICOM standards for camera-acquired dermatologic images to date. To identify and describe existing or proposed technology and technique standards for camera-acquired dermatologic images in the scientific literature. Systematic searches of the PubMed, EMBASE, and Cochrane databases were performed in January 2013 using photography and digital imaging, standardization, and medical specialty and medical illustration search terms and augmented by a gray literature search of 14 websites using Google. Two reviewers independently screened titles of 7371 unique publications, followed by 3 sequential full-text reviews, leading to the selection of 49 publications with the most recent (1985-2013) or detailed description of technology or technique standards related to the acquisition or use of images of skin disease (or related conditions). No universally accepted existing technology or technique standards for camera-based digital images in dermatology were identified. Recommendations are summarized for technology imaging standards, including spatial resolution, color resolution, reproduction (magnification) ratios, postacquisition image processing, color calibration, compression, output, archiving and storage, and security during storage and transmission. Recommendations are also summarized for technique imaging standards, including environmental conditions (lighting, background, and camera position), patient pose and standard view sets, and patient consent, privacy, and confidentiality. Proposed standards for specific-use cases in total body photography, teledermatology, and dermoscopy are described. The literature is replete with descriptions of obtaining photographs of skin disease, but universal imaging standards have not been developed, validated, and adopted to date. Dermatologic imaging is evolving without defined standards for camera-acquired images, leading to variable image quality and limited exchangeability. The development and adoption of universal technology and technique standards may first emerge in scenarios when image use is most associated with a defined clinical benefit.

Development of Diagnostic Reference Levels Using a Real-Time Radiation Dose Monitoring System at a Cardiovascular Center in Korea.

Digital cardiovascular angiography accounts for a major portion of the radiation dose among the examinations performed at cardiovascular centres. However, dose-related information is neither monitored nor recorded systemically. This report concerns the construction of a radiation dose monitoring system based on digital imaging and communications in medicine (DICOM) data and its use at the cardiovascular centre of the University Hospitals in Korea. The dose information was analysed according to DICOM standards for a series of procedures, and the formulation of diagnostic reference levels (DRLs) at our cardiovascular centre represents the first of its kind in Korea. We determined a dose area product (DAP) DRL for coronary angiography of 75.6 Gy cm(2) and a fluoroscopic time DRL of 318.0 s. The DAP DRL for percutaneous transluminal coronary intervention was 213.3 Gy cm(2), and the DRL for fluoroscopic time was 1207.5 s.

Design and Implementation of Standard DICOM Interface Module

DICOM (Digital Imaging and Communications in Medicine) which is standard of PACS(Picture Archiving Communication System) is set up as draft international standard for the purpose of collection and exchange the data and image among main medical display equipments. Medical display equipments is normally utilized as connected feature with surgery monitoring devices like endoscope, X-lay camera. However, most of medical surgery monitoring devices are not adaptable for DICOM, thus it is diffcult to connect data thru PACS. We suggest the connection methodology for PACS and medical surgery monitoring devices by implementation of HL7 interface which can share the medical information with DICOM. The standard DICOM interface module suggested in this thesis creates DICOM IOD (Information Object Definition) information class according to the checkup information received by HIS (Hospital Information System) and also creates standard DICOM format after blending with surgery monitoring data.

Multi-series DICOM: an Extension of DICOM That Stores a Whole Study in a Single Object

Today, most medical images are stored as a set of single-frame composite Digital Imaging and Communications in Medicine (DICOM) objects that contain the four levels of the DICOM information model-patient, study, series, and instance. Although DICOM addresses most of the issues related to medical image archiving, it has some limitations. Replicating the header information with each DICOM object increases the study size and the parsing overhead. Multi-frame DICOM (MFD) was developed to address this, among other issues. The MFD combines all DICOM objects belonging to a series into a single DICOM object. Hence, the series-level attributes are normalized, and the amount of header data repetition is reduced. In this paper, multi-series DICOM (MSD) is introduced as a potential extension to the DICOM standard that allows faster parsing, transmission, and storage of studies. MSD extends the MFD de-duplication of series-level attributes to study-level attributes. A single DICOM object that stores the whole study is proposed. An efficient algorithm, called the one-pass de-duplication algorithm, was developed to find and eliminate the replicated data elements within the study. A group of experiments were done that evaluate MSD and the one-pass de-duplication algorithm performance. The experiments show that MSD significantly reduces the amount of data repetition and decreases the time required to read and parse DICOM studies. MSD is one possible solution that addresses the DICOM limitations regarding header information repetition.

Beam coordinate transformations from DICOM to DOSXYZnrc

Digital imaging and communications in medicine (DICOM) format is the de facto standard for communications between therapeutic and diagnostic modalities. A plan generated by a treatment planning system (TPS) is often exported in DICOM format. BEAMnrc/DOSXYZnrc is a widely used Monte Carlo (MC) package for modelling the Linac head and simulating dose delivery in radiotherapy. It has its own definition of beam orientation, which is not in compliance with the one defined in the DICOM standard. MC dose calculations using information from TPS generated plans require transformation of beam orientations to the DOSXYZnrc coordinate system (c.s.) and the transformation is non-trivial. There have been two studies on the coordinate transformations. The transformation equation sets derived have been helpful to BEAMnrc/DOSXYZnrc users. However, the transformation equation sets are complex mathematically and not easy to program. In this study, we derive a new set of transformation equations, which are more compact, easily understandable, and easier for computational implementation. The derivation of the polar angle θ and the azimuthal angle φ used by DOSXYZnrc is similar to the existing studies by applying a series of rotations to a vector in DICOM patient c.s. The derivation of the beam rotation ϕcol for DOSXYZnrc, however, is different. It is obtained by a direct combination of the actual collimator rotation with the projection of the couch rotation to the collimator rotating plane. Verification of the transformation has been performed using clinical plans. The comparisons between TPS and MC results show very good geometrical agreement for field placements, together with good agreement in dose distributions.