Australian Imaging Service (AIS)

A national federation for securely managing and analysing imaging data
A patient ready to receive a scan
Image — Maksym Povozniuk - 425342467 / AdobeStock.com
Who will benefit
Researchers, imaging specialists, infrastructure providers

The Challenge

Universities and clinical sites across Australia have struggled to manage large volumes of imaging data, while balancing patient privacy and the need for sharing and accessibility in the research community. In recent years, there has been rapid adoption of XNAT, a data management and informatics platform for imaging research, by Australian researchers.

The Response

The Australian Imaging Service (AIS) project was conceived to standardise and integrate deployments of the XNAT platform to create a distributed federation of enhanced XNATs.

The AIS seeks to increase research reproducibility and drive the adoption of innovative but trusted analysis techniques. The aim is a unified service underpinning all imaging research conducted by Australians, both nationally and abroad, on which more specific research and development programs can be built.

The AIS operates as a federation. It co-maintains a central set of software repositories with each partner institution operating their own node according to their local governance, infrastructure and cost structures. The platform integrates with imaging devices in a hub-and-spoke model, where each node integrates their local academic and clinical equipment and data can be transferred between nodes to facilitate multi-site studies.

By adopting and standardising user authentication, instrument integration, data ontologies and mature software tools, the AIS lets researchers and facilities spend more time on innovation. It also allows reuse of national datasets by building a provenance trail from image capture to manipulation.

The AIS integrates analysis and informatics directly into data management. Core features include:

  • secure, audited data management, access and de-identification built around XNAT
  • non-interactive pipelines for repository-centric analysis built using Arcana
  • interactive analysis integrating Jupyter and Neurodesk to provide secure virtual desktops
  • machine learning, starting with MONAI Label, a tool for AI-assisted image annotation and segmentation.

Who Will Benefit

Research organisations and infrastructure providers will benefit from simplified deployment of XNAT across a range of infrastructures. Federated access to data and analysis pipelines across the participating XNAT deployments, provided by a single sign-on through the Australian Access Federation (AAF), will solve significant and urgent data challenges for Australian researchers and imaging specialists, especially those involved in secure clinical, preclinical, veterinary and archaeological imaging.

The AIS has been supporting hundreds of users across over 200 projects and partnering with a large number of programs. These include:

The AIS has also worked with other ARDC-supported projects, including the:

The Partners

  • The University of Sydney
  • National Imaging Facility (NIF)
  • Macquarie University
  • QCIF
  • QUT
  • The University of Queensland
  • UNSW Sydney
  • Neuroscience Research Australia
  • The University of Western Australia
  • SAHMRI

The following institutions advised on the project:

  • Swinburne University
  • Monash University
  • The Florey.

The Outcome

The AIS is transforming imaging data management, analysis, informatics and machine learning. Visit the AIS website to learn more about the project and for technical and user documentation.

Contact the ARDC

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