2026 Projects

The World Health Organization reports that over two-thirds of the global population lacks access to even basic medical imaging services, and many remote and indigenous Australian communities face inequality in accessing medical imaging. There is a pressing need to improve the affordability and accessibility to current and future medical imaging analysis software, especially for socio-economically disadvantaged patients. Many of these patients live remotely and lack adequate access to medical imaging, resulting in delays in treatments.

Ultra-low-field (ULF) MRI (64 mT) provides a portable, low-cost neuroimaging solution critical for improving diagnostic access in resource-limited and remote regions. However, the inherent low Signal-to-Noise Ratio (SNR) and lack of large, labelled pathology datasets impede the development of robust, automated AI screening tools for clinical utilisation of these systems.

This project addresses this barrier by developing an automated anomaly detection toolkit for the ULF MRI. We will leverage datasets acquired from existing funded projects including a 64 mT and 3 T paired dataset with both healthy and disease to develop an anomaly detection tool, termed Normative and UnsupervisedAnomaly Detection (NUAD) to identify both healthy and abnormal brains, facilitating timely diagnosis and triage of patients. The seed funding will deliver a clinically interpretable, open-source UAD pipeline compatible with portable ULF scanners, providing an immediate, scalable mechanism to enhance timely diagnostics for brain injuries and other acute neurological conditions in rural and remote hospitals across Oceania, fulfilling the mandate for geographical health equity.