IHE Teaching File and Clinical Trial Export integration profile: Functional examples
ABSTRACT The digital revolution in radiology introduced the need for electronic export of medical images. However, the current export process is complicated and time consuming. In response to this continued difficulty, the Integrating the Healthcare Enterprise (IHE) initiative published the Teaching File and Clinical Trial Export (TCE) integration profile. The IHE TCE profile describes a method for using existing standards to simplify the export of key medical images for education, research, and publication. This article reviews the authors' experience in implementing the TCE profile in the following three processes: (a) the retrieval of images for a typical teaching file application within a TCE-compliant picture archiving and communication system (PACS); (b) the export of images, independent of TCE compliance of the PACS, to a typical teaching file application; and (c) the TCE-compliant transfer of images for publication. These examples demonstrate methods with which the TCE profile can be implemented to ease the burden of collecting key medical images from the PACS.
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ABSTRACT: Validation of medical signal and image processing systems requires quality-assured, representative and generally acknowledged databases accompanied by appropriate reference (ground truth) and clinical metadata, which are composed laboriously for each project and are not shared with the scientific community. In our vision, such data will be stored centrally in an open repository. We propose an architecture for a standardized case data and ground truth information repository supporting the evaluation and analysis of computer-aided diagnosis based on (a) the Reference Model for an Open Archival Information System (OAIS) provided by the NASA Consultative Committee for Space Data Systems (ISO 14721:2003), (b) the Dublin Core Metadata Initiative (DCMI) Element Set (ISO 15836:2009), (c) the Open Archive Initiative (OAI) Protocol for Metadata Harvesting, and (d) the Image Retrieval in Medical Applications (IRMA) framework. In our implementation, a portal bunches all of the functionalities that are needed for data submission and retrieval. The complete life cycle of the data (define, create, store, sustain, share, use, and improve) is managed. Sophisticated search tools make it easier to use the datasets, which may be merged from different providers. An integrated history record guarantees reproducibility. A standardized creation report is generated with a permanent digital object identifier. This creation report must be referenced by all of the data users. Peer-reviewed e-publishing of these reports will create a reputation for the data contributors and will form de-facto standards regarding image and signal datasets. Good practice guidelines for validation methodology complement the concept of the case repository. This procedure will increase the comparability of evaluation studies for medical signal and image processing methods and applications.Journal of Digital Imaging 11/2011; 25(2):213-26. DOI:10.1007/s10278-011-9428-4 · 1.20 Impact Factor
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ABSTRACT: RATIONALE AND OBJECTIVES: Chest radiographs can be demanding, making this an area of focus during most first-year resident chest rotations. This often comes at a cost of cross-sectional imaging, and new residents are often not initially comfortable with reading chest computed tomographic angiograms (CTAs) for pulmonary embolisms (PEs). We created a teaching file of CTAs to improve the detection of PEs. MATERIALS AND METHODS: For initial testing, we used videos of 25 cases, which played for 90 seconds (to allow multiple passes) to residents with and without call experience. The presence and location of PEs and the readers' confidence scores were recorded. After initial testing, first-year residents without call experience were given 20 separate known positive CTA videos to scroll through on their own. The goal of this was to allow for individual review and development of individual search strategies. A second testing was done with all levels of residents with the same initial 25 cases, re-randomized to evaluate for improvement. RESULTS: Initially, first-year residents without call experience identified an average of 14.7 of 18 examinations positive for PEs (versus 15.8 for more senior residents; P < .04). After reviewing the 20 known positive cases, the first-year residents improved, averaging 16.6 (versus 14.7 earlier; P < .01). CONCLUSIONS: We created a fast, simple way to expose novice residents to CTA examinations and increase their accuracy in identifying PEs. After using a teaching file, the ability to recognize PEs improved significantly, and scores were no longer significantly different from those of residents with call experience.Academic radiology 03/2013; DOI:10.1016/j.acra.2012.12.020 · 2.08 Impact Factor
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ABSTRACT: Radiologists come across interesting patient cases almost every day. This work proposes a novel case database server for quick and easy storage of such cases including whole image series, patient data, and annotations. Cases can be added to the database by saving DICOM images into a predefined directory on the local network. The application automatically extracts patient and study data from the DICOM header and saves it in the database while images are stored as anonymized JPEG files. Users can mark their cases as private or public (visible to all users). Different data fields for annotations and categorization of a case are available. The user frontend also provides several retrieval mechanisms allowing for browsing the cases and performing different kinds of search queries. The stored series can be scrolled interactively in the form of scrollable image stacks. The project is realized as a web-based application using a portable web and database server software package (XAMPP). This makes the system very lightweight and easy to run on almost any desktop computer, even from a USB flash drive, without the need for deeper IT knowledge and administrative rights.Journal of Digital Imaging 05/2014; 27(5). DOI:10.1007/s10278-014-9691-2 · 1.20 Impact Factor