Integrating Medical Imaging Analyses through a High-throughput Bundled Resource Imaging System

Area of Science:

• Medical Imaging
• Computer Science
• Bioinformatics

Background:

• Picture Archiving and Communication Systems (PACS) offer robust storage and retrieval but can limit the integration of new research technologies.
• Evaluating emerging medical imaging research tools within established PACS environments presents significant validation and integration challenges.

Purpose of the Study:

• To present a high-throughput bundled resource imaging system (HUBRIS) as a solution to integrate and evaluate new research technologies within PACS.
• To demonstrate how HUBRIS facilitates the use of advanced imaging analysis tools with existing medical imaging equipment and PACS.

Main Methods:

• HUBRIS was developed as an extension to the Philips Research Imaging Development Environment (PRIDE).
• It utilizes the Java Imaging Science Toolkit (JIST) to enable PACS-connected imaging equipment to invoke research algorithms.
• A grid computing facility is used to execute selected algorithms, with results returned to the imaging platform and PACS.

Main Results:

• HUBRIS allows medical imaging platforms to pass images and parameters to a server for algorithm execution.
• Generated images are successfully passed back through the system to the imaging platform and can be sent to PACS.
• The Java Imaging Science Toolkit (JIST) provides an open API, supporting implementation in various programming languages.

Conclusions:

• HUBRIS simplifies the evaluation and analysis of emerging imaging technologies within established PACS.
• This approach minimizes the need for research software adaptation, streamlining clinical research.
• HUBRIS enhances the usability of PACS technology for imaging scientists exploring new tools.