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Status : Active
Research Topic : Particle physics
Socio-Economic Objective : Medical Instruments
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP230101327

    Funder
    Australian Research Council
    Funding Amount
    $476,000.00
    Summary
    Dark-field: A new kind of x-ray imaging. This project aims to develop new x-ray imaging capabilities that look inside an object and map out those details that are too small to be seen directly, by extracting the dark-field which is produced as x-ray light scatters. Dark-field images can reveal tiny cracks in manufactured parts, discover powdered explosives or drugs during security screening, and detect changes in the size of the many tiny air sacs in the lungs. Expected outcomes of this project .... Dark-field: A new kind of x-ray imaging. This project aims to develop new x-ray imaging capabilities that look inside an object and map out those details that are too small to be seen directly, by extracting the dark-field which is produced as x-ray light scatters. Dark-field images can reveal tiny cracks in manufactured parts, discover powdered explosives or drugs during security screening, and detect changes in the size of the many tiny air sacs in the lungs. Expected outcomes of this project include new instruments and methods of analysis that will allow x-ray dark-field imaging to be quantitative and widely adopted. These methods should benefit non-invasive multi-scale imaging at the Australian Synchrotron and equip x-ray imaging in industry, security and healthcare.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT180100374

    Funder
    Australian Research Council
    Funding Amount
    $738,125.00
    Summary
    Dynamic multi-modal x-ray imaging. This project aims to create sensitive new methods of x-ray imaging that capture multiple image modalities with a single snapshot. Conventional x-ray imaging is widely used in a range of industries, but captures only a fraction of the rich information that is available in the x-ray wavefield. This project expects to extract additional image modalities to reveal x-ray-transparent features, and detect microscopic textures. By combining these capabilities with the .... Dynamic multi-modal x-ray imaging. This project aims to create sensitive new methods of x-ray imaging that capture multiple image modalities with a single snapshot. Conventional x-ray imaging is widely used in a range of industries, but captures only a fraction of the rich information that is available in the x-ray wavefield. This project expects to extract additional image modalities to reveal x-ray-transparent features, and detect microscopic textures. By combining these capabilities with the ability to capture images of a moving sample, this project will enable innovative biomedical and materials research studies, and develop new imaging technologies for use in security, hospitals and manufacturing. New methods of x-ray imaging will have wide-ranging benefits for society, the economy and healthcare.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT200100154

    Funder
    Australian Research Council
    Funding Amount
    $778,452.00
    Summary
    Next generation photonic waveguide sensors enabled by machine learning. This project aims to establish the next frontier in photonic waveguide sensing, by using machine learning to shift the complexity out of conventional photonic-waveguide/optical-fibre sensors and into smart detection algorithms. The complexity and instability of multimode photonic waveguides, traditionally a hinderance to sensing, will be advantageously employed to train deep learning models for sensing. Expected outcomes inc .... Next generation photonic waveguide sensors enabled by machine learning. This project aims to establish the next frontier in photonic waveguide sensing, by using machine learning to shift the complexity out of conventional photonic-waveguide/optical-fibre sensors and into smart detection algorithms. The complexity and instability of multimode photonic waveguides, traditionally a hinderance to sensing, will be advantageously employed to train deep learning models for sensing. Expected outcomes include the creation of intelligent photonic sensors that can, in principle, measure any environmental parameter using any optical waveguide material. It will create new critically needed measurement capabilities for challenging harsh environments, such as extreme temperature and in-vivo biochemical sensing.
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