CytometryML and other data formats

Over the past 15 years Digital Imaging and Communications in Medicine (DICOM) has established itself as the international standard for medical image communication. Most medical imaging equipment uses DICOM network and media services to export image data, thus making this standard highly relevant for medical image processing. The first section of this chapter provides a basic introduction into DICOM with its more than 3,600 pages of technical documentation, followed by a section covering selected advanced topics of special interest for medical image processing. The introductory text familiarizes the reader with the standard’s main concepts such as information objects and DICOM media and network services. The rendering pipeline for image display and the concept of DICOM conformance are also discussed. Specialized DICOM services such as advanced image display services that provide means for storing how an image was viewed (“Softcopy Presentation States”) and how multiple images should be aligned on an output device (“Structured Display” and “Hanging Protocols”) are described. We further describe DICOM’s sophisticated approach (“Structured Reporting”) for storing structured documents such as CAD information, which is then covered in more detail. Finally, the last section provides an insight into a newly developed DICOM service called “Application Hosting”, which introduces a standardized plug-in architecture for image processing, thus permitting users to utilize cross-vendor image processing plug-ins in DICOM applications.

Providing eligibility, efficacy and security evaluation by quantitative and qualitative disease findings, medical imaging has become increasingly important in clinical trials. Here, subject’s data is today captured in electronic case reports forms (eCRFs), which are offered by electronic data capture (EDC) systems. However, integration of subject’s medical image data into eCRFs is insufficiently supported. Neither integration of subject’s digital imaging and communications in medicine (DICOM) data, nor communication with picture archiving and communication systems (PACS), is possible. This aggravates the workflow of the study personnel, in special regarding studies with distributed data capture in multiple sites. Hence, in this work, a system architecture is presented, which connects an EDC system, a PACS and a DICOM viewer via the web access to DICOM objects (WADO) protocol. The architecture is implemented using the open source tools OpenClinica, DCM4CHEE and Weasis. The eCRF forms the primary endpoint for the study personnel, where subject’s image data is stored and retrieved. Background communication with the PACS is completely hidden for the users. Data privacy and consistency is ensured by automatic de-identification and re-labelling of DICOM data with context information (e.g. study and subject identifiers), respectively. The system is exemplarily demonstrated in a clinical trial, where computer tomography (CT) data is de-centrally captured from the subjects and centrally read by a chief radiologists to decide on inclusion of the subjects in the trial. Errors, latency and costs in the EDC workflow are reduced, while, a research database is implicitly built up in the background.

4 - Digital Imaging and Communications in Medicine

Background The current gap between the availability of routine imaging data and its provisioning for medical research hinders the utilization of radiological information for secondary purposes. To address this, the German Medical Informatics Initiative (MII) has established frameworks for harmonizing and integrating clinical data across institutions, including the integration of imaging data into research repositories, which can be expanded to routine imaging data.Objectives This project aims to address this gap by developing a large-scale data processing pipeline to extract, convert, and pseudonymize DICOM (Digital Imaging and Communications in Medicine) metadata into “ImagingStudy” Fast Healthcare Interoperability Resources (FHIR) and integrate them into research repositories for secondary use.Methods The data processing pipeline was developed, implemented, and tested at the Data Integration Center of the University Hospital Erlangen. It leverages existing open-source solutions and integrates seamlessly into the hospital's research IT infrastructure. The pipeline automates the extraction, conversion, and pseudonymization processes, ensuring compliance with both local and MII data protection standards. A large-scale evaluation was conducted using the imaging studies acquired by two departments at University Hospital Erlangen within 1 year. Attributes such as modality, examined body region, laterality, and the number of series and instances were analyzed to assess the quality and availability of the metadata.Results Once established, the pipeline processed a substantial dataset comprising over 150,000 DICOM studies within an operational period of 26 days. Data analysis revealed significant heterogeneity and incompleteness in certain attributes, particularly the DICOM tag “Body Part Examined.” Despite these challenges, the pipeline successfully generated valid and standardized FHIR, providing a robust basis for future research.Conclusion We demonstrated the setup and test of a large-scale end-to-end data processing pipeline that transforms DICOM imaging metadata directly from clinical routine into the Health Level 7-FHIR format, pseudonymizes the resources, and stores them in an FHIR server. We showcased that the derived FHIRs offer numerous research opportunities, for example, feasibility assessments within Bavarian and Germany-wide research infrastructures. Insights from this study highlight the need to extend the “ImagingStudy” FHIR with additional attributes and refine their use within the German MII.

Picture archiving and communication systems traditionally have depended on schema-based Structured Query Language (SQL) databases for imaging data management. To optimize database size and performance, many such systems store a reduced set of Digital Imaging and Communications in Medicine (DICOM) metadata, discarding informational content that might be needed in the future. As an alternative to traditional database systems, document-based key-value stores recently have gained popularity. These systems store documents containing key-value pairs that facilitate data searches without predefined schemas. Document-based key-value stores are especially suited to archive DICOM objects because DICOM metadata are highly heterogeneous collections of tag-value pairs conveying specific information about imaging modalities, acquisition protocols, and vendor-supported postprocessing options. The authors used an open-source document-based database management system (Apache CouchDB) to create and test two such databases; CouchDB was selected for its overall ease of use, capability for managing attachments, and reliance on HTTP and Representational State Transfer standards for accessing and retrieving data. A large database was created first in which the DICOM metadata from 5880 anonymized magnetic resonance imaging studies (1,949,753 images) were loaded by using a Ruby script. To provide the usual DICOM query functionality, several predefined "views" (standard queries) were created by using JavaScript. For performance comparison, the same queries were executed in both the CouchDB database and a SQL-based DICOM archive. The capabilities of CouchDB for attachment management and database replication were separately assessed in tests of a similar, smaller database. Results showed that CouchDB allowed efficient storage and interrogation of all DICOM objects; with the use of information retrieval algorithms such as map-reduce, all the DICOM metadata stored in the large database were searchable with only a minimal increase in retrieval time over that with the traditional database management system. Results also indicated possible uses for document-based databases in data mining applications such as dose monitoring, quality assurance, and protocol optimization.

The integration of cytomics research and healthcare informatics will facilitate technology transfer and reduce medical costs. The CytometryML prototype of the Advanced Cytometry Standard (ACS) has the benefits of including microscopic image and flow list-mode data, being based on XML and thus is compatible with existing medical and scientific informatics standards, such as HL7, and employing a design based upon the Digital Imaging and Communications in Medicine (DICOM) standard. The reuse of the well tested DICOM model resulted in a great decrease in the design and documentation effort and increased probability of reliability. Schemas for flow cytometers and microscopes have been created. XML schemas for two related types of container (ZIP) files have been specified for a set of measurements. The series and instance containers respectively include the metadata that is constant and the metadata that is specific to an individual or small closely related group of measurements.

The digital imaging and communications in medicine (DICOM) protocol is nowadays the leading standard for capture, exchange and storage of image data in medical applications. A broad range of commercial, free, and open source software tools supporting a variety of DICOM functionality exists. However, different from patient's care in hospital, DICOM has not yet arrived in electronic data capture systems (EDCS) for clinical trials. Due to missing integration, even just the visualization of patient's image data in electronic case report forms (eCRFs) is impossible. Four increasing levels for integration of DICOM components into EDCS are conceivable, raising functionality but also demands on interfaces with each level. Hence, in this paper, a comprehensive evaluation of 27 DICOM viewer software projects is performed, investigating viewing functionality as well as interfaces for integration. Concerning general, integration, and viewing requirements the survey involves the criteria (i) license, (ii) support, (iii) platform, (iv) interfaces, (v) two-dimensional (2D) and (vi) three-dimensional (3D) image viewing functionality. Optimal viewers are suggested for applications in clinical trials for 3D imaging, hospital communication, and workflow. Focusing on open source solutions, the viewers ImageJ and MicroView are superior for 3D visualization, whereas GingkoCADx is advantageous for hospital integration. Concerning workflow optimization in multi-centered clinical trials, we suggest the open source viewer Weasis. Covering most use cases, an EDCS and PACS interconnection with Weasis is suggested.

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

Online public repositories for sharing research data allow investigators to validate existing research or perform secondary research without the expense of collecting new data. Patient data made publicly available through such repositories may constitute a breach of personally identifiable information if not properly de-identified. Imaging data are especially at risk because some intricacies of the Digital Imaging and Communications in Medicine (DICOM) format are not widely understood by researchers. If imaging data still containing protected health information (PHI) were released through a public repository, a number of different parties could be held liable, including the original researcher who collected and submitted the data, the original researcher's institution, and the organization managing the repository. To minimize these risks through proper de-identification of image data, one must understand what PHI exists and where that PHI resides, and one must have the tools to remove PHI without compromising the scientific integrity of the data. DICOM public elements are defined by the DICOM Standard. Modality vendors use private elements to encode acquisition parameters that are not yet defined by the DICOM Standard, or the vendor may not have updated an existing software product after DICOM defined new public elements. Because private elements are not standardized, a common de-identification practice is to delete all private elements, removing scientifically useful data as well as PHI. Researchers and publishers of imaging data can use the tools and process described in this article to de-identify DICOM images according to current best practices.

In the past, many of the medical information produced by hospitals (such as medical records or imagery) have had trouble being transferred from place to place; the main reason for this being the fference in the many formats that are used by each and every brand. With the development of the ages, the need for exchange of medical information has increased. Thus, imagery medical information uses the DICOM (Digital Image and Communication in Medicine) standard. The goal of this study is to establish a reformatting interface using the DICOM file management system to transfer paper-based records into DICOM format. Once in DICOM format, these records can then be sent to the DICOM server so that they may be accessed via the DICOM Viewer cross hospitals. This study also aims to transform text information into DICOM format so that files may be exchanged with other hospitals through DICOM’s secure system so that medical records can be incorporated into hospitals’ image systems to form a congregation of hospital resources.

A number of clinical specialties routinely use images in treating patients, for example ophthalmology, dentistry, cardiology, endoscopy, and surgery. These images are captured by a variety of commercial digital image acquisition systems. The US Department of Veterans Affairs has been working for several years on advancing the use of the Digital Imaging and Communications in Medicine (DICOM) Standard in these clinical specialties. This is an effort that has involved several facets: (1) working with the vendors to ensure that they satisfy existing DICOM requirements, (2) developing interface software to the VistA hospital information system (HIS), (3) field testing DICOM systems, (4) deploying these DICOM interfaces nation-wide to all VA medical centers, (5) working with the healthcare providers using the system, and (6) participating in the DICOM working groups to improve the standard. The VA is now beginning to develop clinical applications that make use of the DICOM interfaces in the clinical specialties. The first of these will be in ophthalmology to remotely screen patients for diabetic retinopathy.

Since May 2002, temporal subtraction and nodule detection systems for digital chest radiographs have been integrated into our hospital's picture archiving and communication systems (PACS). Image data of digital chest radiographs were stored in PACS with the digital image and communication in medicine (DICOM) protocol. Temporal subtraction and nodule detection images were produced automatically in an exclusive server and delivered with current and previous images to the work stations. The problems that we faced and the solutions that we arrived at were analyzed. We encountered four major problems. The first problem, as a result of the storage of the original images' data with the upside-down, reverse, or lying-down positioning on portable chest radiographs, was solved by postponing the original data storage for 30 min. The second problem, the variable matrix sizes of chest radiographs obtained with flat-panel detectors (FPDs), was solved by improving the computer algorithm to produce consistent temporal subtraction images. The third problem, the production of temporal subtraction images of low quality, could not be solved fundamentally when the original images were obtained with different modalities. The fourth problem, an excessive false-positive rate on the nodule detection system, was solved by adjusting this system to chest radiographs obtained in our hospital. Integration of the temporal subtraction and nodule detection system into our hospital's PACS was customized successfully; this experience may be helpful to other hospitals.

Digital Imaging and Communications in Medicine (DICOM) is a standard for handling, storing, printing, and transmitting information in medical images. The DICOM file contains the image data and a number of attributes such as identified patient data (name, age, insurance ID card,…), and non-identified patient data (doctor’s interpretation, image type,…). Medical images serve not only for examination, but can also be used for research and education purposes. For research they are used to prevent illegal use of information; before authorizing researchers to use these images, the medical staff deletes all the data which would reveal the patient identity to prevent patient privacy. This manipulation is called anonymization. In this paper, we propose a reversible anonymization of DICOM images. Identifying patient data with image digest, computed by the well-known SHA-256 hash function, are encrypted using the proposed probabilistic public key crypto-system. After compressing the Least Significant Bit (LSB) bitplan of the image using Hofmann coding algorithm, the encrypted data is inserted into a liberated zone of the LSB bitplan of the image. The proposed method allows researchers to use anonymous DICOM images and keep to authorized staff -if necessary- the possibility to return to the original image with all related patient data.

Over the last decade, the specification of a standard format for digital (medical) images has received a great deal of attention. The yearly meetings of the ACC (95 and 96) and ESC (95) were used to promote the DICOM (Digital Imaging and Communications in Medicine) format, the result of a joined effort by the ACC, ACR, NEMA and ESC. At the moment, one of the most interesting questions is: how will the introduction of the DICOM standard affect the daily practice in the catheterization laboratory (cath lab) in the near future?

Following the lead of the American College of Radiology, the DICOM (Digital Imaging and Communication in Medicine) standard was recently adopted by the cardiology community, and implementations of this standard for cardiovascular imaging systems are now emerging. DICOM offers the promise of exchangeability of medical images from different manufacturers and across heterogeneous systems. To ensure that we achieve this goal and the proliferation of the standard, it is important to make robust and flexible implementations available to system developers. This paper presents an object-oriented design and implementation of a class hierarchy for the exchange of X-ray angiographic image data using the DICOM standard. The software has been implemented as a toolkit of objects written in the C++ programming language, and is currently being used in the validation of a system for remote consultation of cardiovascular image sequences as part of a European Community project.