foundingGIDE at The Australian National Imaging Facility Annual Scientific Meeting 2024
The Australian National Imaging Facility Annual Scientific Meeting 2024 (NIF ASM) brought together the Australian imaging community from August 7 to 9 in Brisbane, Australia. The event was hosted by Herston Imaging Research Facility and the University of Queensland, one of the foundingGIDE project partners. Several members of the project attended the meeting, including Peter Bugeia and Ryan Sullivan.
NIF ASM is an event showcasing world-leading expertise, cutting-edge imaging technologies, and emerging medical and scientific discoveries. The 2024 meeting featured advanced imaging techniques, transformative applications spanning from preclinical to human research, imaging data management and analysis techniques, and interactive sessions designed to foster collaboration.
Among the many projects at the NIF ASM, foundingGIDE project was highlighted by Ryan Sullivan. As the lead of the Australian Imaging Service (AIS) at the University of Sydney, Ryan Sullivan is at the forefront of this national initiative, supported by Sydney Imaging staff, including Thomas Close, a NIF fellow, who directs the AIS integrated analysis pipelines team. AIS is focused on standardising and integrating the XNAT platform for imaging research across a distributed network.
XNAT is an open source imaging informatics platform developed by the Neuroinformatics Research Group at Washington University. Metadata on any imaging platform plays a crucial role in ensuring that imaging data is properly described, categorised, and made interoperable across different systems and research projects.
Preclinical image datasets encompass a wide range of information across multiple domains, offering significant potential for reuse. However, these datasets are often stored without the necessary additional information or metadata, which hinders their discovery and interpretation.
In collaboration with the preclinical imaging community, foundingGIDE focuses on developing a preclinical imaging metadata model recommendation and a set of tools for collecting and storing metadata on the XNAT platform, ensuring that all essential information needed to properly describe a preclinical image dataset is captured. This work includes the incorporation of preclinical metadata schemas into the AIS framework.