Industrial Transformation Research Hubs - Grant ID: IH150100028
Funder
Australian Research Council
Funding Amount
$3,708,510.00
Summary
ARC Research Hub for Integrated Device for End-user Analysis at Low-levels. ARC Research Hub for Integrated Device for End-user Analysis at Low-levels. This hub aims to improve detection of biological materials by building a portable device for rapid, time-critical detection of low-abundance molecular and cellular analytes. It is expected that the resulting technologies would be used at medical points of care, ordinary workplaces and centres of activity to test for tiny levels of targeted molecu ....ARC Research Hub for Integrated Device for End-user Analysis at Low-levels. ARC Research Hub for Integrated Device for End-user Analysis at Low-levels. This hub aims to improve detection of biological materials by building a portable device for rapid, time-critical detection of low-abundance molecular and cellular analytes. It is expected that the resulting technologies would be used at medical points of care, ordinary workplaces and centres of activity to test for tiny levels of targeted molecules. The initial focus would be early diagnosis of disease and point-of-care drug testing for humans and animals, but the technology platform could be used to sample food and environmental toxins. The hub expects these disruptive technologies will make Australian biotechnology, diagnostics, veterinary, agribusiness and manufacturing firms globally competitive.Read moreRead less
Biophysics-informed deep learning framework for magnetic resonance imaging. This project aims to bring about a paradigm shift from the conventional non-quantitative magnetic resonance imaging to ultra-fast, quantitative, and artefact free imaging. This project integrates biophysics and artificial intelligence, and it is expected to bring new knowledge in both fields. The expected outcomes of this project include next generation magnetic resonance imaging methods with a fundamental shift in the ....Biophysics-informed deep learning framework for magnetic resonance imaging. This project aims to bring about a paradigm shift from the conventional non-quantitative magnetic resonance imaging to ultra-fast, quantitative, and artefact free imaging. This project integrates biophysics and artificial intelligence, and it is expected to bring new knowledge in both fields. The expected outcomes of this project include next generation magnetic resonance imaging methods with a fundamental shift in the approach to image artefacts and image quantification. This project is expected to advance both single subject and population level biomedical imaging with greater accuracy and cost-effectiveness. This project also promotes explainable and generalisable artificial intelligence in medical imaging.Read moreRead less