Journal:Design, implementation and operation of a multimodality research imaging informatics repository

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Full article title Design, implementation and operation of a multimodality research imaging informatics repository
Journal Health Information Science and Systems
Author(s) Nguyen, Toan D.; Raniga, Parnesh; Barnes, David G.; Egan, Gary F.
Author affiliation(s) Monash University, CSIRO
Primary contact Email:
Year published 2015
Volume and issue 3 (Suppl 1)
Page(s) S6
DOI 10.1186/2047-2501-3-S1-S6
ISSN 2047-2501
Distribution license Creative Commons Attribution 4.0 International
Download (PDF)


Background: Biomedical imaging research increasingly involves acquiring, managing and processing large amounts of distributed imaging data. Integrated systems that combine data, meta-data and workflows are crucial for realising the opportunities presented by advances in imaging facilities.

Methods: This paper describes the design, implementation and operation of a multi-modality research imaging data management system that manages imaging data obtained from biomedical imaging scanners operated at Monash Biomedical Imaging (MBI), Monash University in Melbourne, Australia. In addition to Digital Imaging and Communications in Medicine (DICOM) images, raw data and non-DICOM biomedical data can be archived and distributed by the system. Imaging data are annotated with meta-data according to a study-centric data model and, therefore, scientific users can find, download and process data easily.

Results: The research imaging data management system ensures long-term usability, integrity inter-operability and integration of large imaging data. Research users can securely browse and download stored images and data, and upload processed data via subject-oriented informatics frameworks including the Distributed and Reflective Informatics System (DaRIS), and the Extensible Neuroimaging Archive Toolkit (XNAT).


Modern clinical and biomedical research is increasingly reliant on imaging across a range of electromagnetic and acoustic wavelengths.[1][2][3] Contemporary studies now routinely collect images from more than one type of instrumentation - multi-modal studies[4] - and strive to obtain high spatial and/or temporal resolution data. Multi-modal datasets provide complementary information[5] and enable sophisticated, multivariate analysis, while high-resolution datasets provide insight that was not possible only a few years ago. Extremely large multi-modal imaging studies can result in terabyte (TB) size data collections[6], although most research studies generate data in the megabyte (MB) to gigabyte (GB) range per subject.

The data volume per subject is multiplied by the increasing number of subjects per study. Many of today's high profile biomedical imaging studies have hundreds to thousands of participants.[7][8][9] Furthermore, many of these studies are longitudinal in nature and thus collect imaging data at multiple time points per subject. This multiplier effect results in a large collection of data that must be recorded per subject. Along with the imaging data, non-imaging and meta-data may also collected and should be stored and directly associated with the image data, especially if the data will be mined and/or shared.[10]


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This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. In one case a direct reference to a citation number was changed to reference the author names instead.