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A DICOM-based 2nd generation Molecular Imaging Data Grid implementing the IHE XDS-i integration profile.

Jasper Lee1, Jianguo Zhang, Ryan Park

  • 1Image Processing and Informatics Laboratory, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 734 West Adams Blvd., Los Angeles, CA 90089, USA. jasperle@usc.edu

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The 2nd generation Molecular Imaging Data Grid (MIDG) enhances preclinical research by improving access to animal imaging data. This new system offers streamlined data sharing and management for multi-center studies.

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Area of Science:

  • Biomedical imaging informatics
  • Preclinical research data management
  • Translational science informatics

Background:

  • Current informatics challenges hinder archival, sharing, and distribution of preclinical imaging studies.
  • Existing systems lack efficient data management for multi-center animal imaging research.
  • Bridging the gap between animal imaging facilities and investigator sites is crucial for translational science.

Purpose of the Study:

  • To present the 2nd generation Molecular Imaging Data Grid (MIDG).
  • To address informatics challenges in preclinical imaging data archival, sharing, search, and distribution.
  • To replace the Globus Toolkit with a new architecture implementing the IHE XDS-i integration profile.

Main Methods:

  • Developed a new system architecture for the MIDG, replacing the Globus Toolkit with web services and XML-based messaging.
  • Adopted the Cross-enterprise Document Sharing for Imaging (XDS-i) integration profile for streamlined dataflow.
  • Implemented and evaluated the MIDG across a 3-site interdisciplinary test-bed at the University of Southern California.

Main Results:

  • Evaluation included data upload, download, and fault-tolerance testing with multi-modality animal imaging datasets.
  • Upload and download times demonstrated reproducibility and improved real-world performance.
  • Fault-tolerance tests confirmed minimal impact on download times due to automated server failover.

Conclusions:

  • The 2nd generation MIDG improves accessibility of animal imaging datasets beyond local networks.
  • The new architecture, dataflow, and DICOM-based web services enhance data management and distribution.
  • Productivity and efficiency for translational science investigators are streamlined for multi-center study data handling.