Integrating medical imaging analyses through a high-throughput bundled resource imaging system

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24 March 2011 Integrating medical imaging analyses through a high-throughput bundled resource imaging system

Kelsie Covington, E. Brian Welch, Ha-Kyu Jeong, Bennett A. Landman

Proceedings Volume 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications; 79670E (2011) https://doi.org/10.1117/12.878371
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Exploitation of advanced, PACS-centric image analysis and interpretation pipelines provides well-developed storage, retrieval, and archival capabilities along with state-of-the-art data providence, visualization, and clinical collaboration technologies. However, pursuit of integrated medical imaging analysis through a PACS environment can be limiting in terms of the overhead required to validate, evaluate and integrate emerging research technologies. Herein, we address this challenge through presentation of a high-throughput bundled resource imaging system (HUBRIS) as an extension to the Philips Research Imaging Development Environment (PRIDE). HUBRIS enables PACS-connected medical imaging equipment to invoke tools provided by the Java Imaging Science Toolkit (JIST) so that a medical imaging platform (e.g., a magnetic resonance imaging scanner) can pass images and parameters to a server, which communicates with a grid computing facility to invoke the selected algorithms. Generated images are passed back to the server and subsequently to the imaging platform from which the images can be sent to a PACS. JIST makes use of an open application program interface layer so that research technologies can be implemented in any language capable of communicating through a system shell environment (e.g., Matlab, Java, C/C++, Perl, LISP, etc.). As demonstrated in this proof-of-concept approach, HUBRIS enables evaluation and analysis of emerging technologies within well-developed PACS systems with minimal adaptation of research software, which simplifies evaluation of new technologies in clinical research and provides a more convenient use of PACS technology by imaging scientists.

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Kelsie Covington, E. Brian Welch, Ha-Kyu Jeong, and Bennett A. Landman "Integrating medical imaging analyses through a high-throughput bundled resource imaging system", Proc. SPIE 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 79670E (24 March 2011); https://doi.org/10.1117/12.878371

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