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Proceedings Paper

Application development environment for advanced digital workstations
Author(s): Daniel J. Valentino; Michael R. Harreld; Brent J. Liu; Matthew S. Brown; Lu J. Huang
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Paper Abstract

One remaining barrier to the clinical acceptance of electronic imaging and information systems is the difficulty in providing intuitive access to the information needed for a specific clinical task (such as reaching a diagnosis or tracking clinical progress). The purpose of this research was to create a development environment that enables the design and implementation of advanced digital imaging workstations. We used formal data and process modeling to identify the diagnostic and quantitative data that radiologists use and the tasks that they typically perform to make clinical decisions. We studied a diverse range of radiology applications, including diagnostic neuroradiology in an academic medical center, pediatric radiology in a children's hospital, screening mammography in a breast cancer center, and thoracic radiology consultation for an oncology clinic. We used object- oriented analysis to develop software toolkits that enable a programmer to rapidly implement applications that closely match clinical tasks. The toolkits support browsing patient information, integrating patient images and reports, manipulating images, and making quantitative measurements on images. Collectively, we refer to these toolkits as the UCLA Digital ViewBox toolkit (ViewBox/Tk). We used the ViewBox/Tk to rapidly prototype and develop a number of diverse medical imaging applications. Our task-based toolkit approach enabled rapid and iterative prototyping of workstations that matched clinical tasks. The toolkit functionality and performance provided a 'hands-on' feeling for manipulating images, and for accessing textual information and reports. The toolkits directly support a new concept for protocol based-reading of diagnostic studies. The design supports the implementation of network-based application services (e.g., prefetching, workflow management, and post-processing) that will facilitate the development of future clinical applications.

Paper Details

Date Published: 26 June 1998
PDF: 8 pages
Proc. SPIE 3335, Medical Imaging 1998: Image Display, (26 June 1998); doi: 10.1117/12.312538
Show Author Affiliations
Daniel J. Valentino, UCLA School of Medicine (United States)
Michael R. Harreld, UCLA School of Medicine (United States)
Brent J. Liu, UCLA School of Medicine (United States)
Matthew S. Brown, UCLA School of Medicine (United States)
Lu J. Huang, UCLA School of Medicine (United States)

Published in SPIE Proceedings Vol. 3335:
Medical Imaging 1998: Image Display
Yongmin Kim; Seong Ki Mun, Editor(s)

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