Integrating Medical Imaging Analyses through a High-throughput Bundled Resource Imaging System Kelsie Covington a , E. Brian Welch b,c , Ha-Kyu Jeong b , Bennett A. Landman a,b,c a Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235 b Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235 c Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA 37235 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. Keywords: Image Processing, PACS, MRI, JAVA, XML, WDSL INTRODUCTION Picture Archiving and Communication Systems (PACS) enables systematic and verifiable tracking, access, visualization, and data providence for medical imaging data throughout the healthcare information management system from acquisition to interpretation, especially for three- and higher- dimensional imaging techniques, such as magnetic resonance imaging [1]. These pervasive informatics platforms serve as ideal targets in which to integrate and evaluate new analyses and techniques for eventual inclusion in patient care [2]. Advanced integration of PACS system throughout the entire chain of care is an active area of informatics research and engineering development [3]. A variety of imaging frameworks have emerged to fit the needs of research groups both in terms of functionality and visualization capabilities [4-7], while a multitude of specialized image analysis techniques remain largely in the hands of their creators and collaborators. Integrating these techniques, and bridging the gap between these emerging image analysis technologies and those technologies ready for clinical research and patient trials, is a monumental effort [8-11]. Here, we explore a set of technologies which could allow for earlier feedback of clinical collaborators on emerging technologies and facilitate collaborations between imaging researchers, who typically develop methods using custom software platforms, and clinical researchers, who typically prefer established visualization and analysis approaches afforded by PACS-centric systems. Custom image post-processing in the clinical as well as the research medical imaging setting is often limited by the separation of the processing from the image acquisition hardware platform that connects to the institution’s PACS. The separation decreases the chances that the post-processed images will ever be entered into the PACS and creates an obstacle for routine usage and acceptance of the available post-processing algorithms. For example, novel image reconstruction techniques often require substantive changes to the software pipeline within the image reconstruction