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Field of Research : Biomedical imaging
Socio-Economic Objective : Expanding Knowledge In Engineering
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP230101628

    Funder
    Australian Research Council
    Funding Amount
    $512,607.00
    Summary
    Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging. This project aims to develop novel magnetic resonance imaging methods to investigate tissue structure and function. Current MRI technologies use standard water-based contrast mechanisms to generate images with limited tissue information. In contrast, this project expects to provide a non-invasive, ultra-high-resolution MRI technology that measures the electrical, magnetic, and chemical signals generated from the human bo .... Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging. This project aims to develop novel magnetic resonance imaging methods to investigate tissue structure and function. Current MRI technologies use standard water-based contrast mechanisms to generate images with limited tissue information. In contrast, this project expects to provide a non-invasive, ultra-high-resolution MRI technology that measures the electrical, magnetic, and chemical signals generated from the human body. Thus, the new imaging methods can probe deeper biological functionality while examining tissue structure. The potential benefits include: expanding the scope and capabilities of current MRI, facilitating a wide range of imaging-based research and applications, and accelerating knowledge expansion in life science.
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    Active Funded Activity

    Mid-Career Industry Fellowships - Grant ID: IM230100002

    Funder
    Australian Research Council
    Funding Amount
    $1,056,049.00
    Summary
    Artificial intelligence empowered multi-modal biomedical imaging. This Industry Fellowship aims to transform biomedical imaging using artificial intelligence with world-leading industry partners. The project expects to make a major advance in multi-modal Magnetic Resonance Imaging and Positron Emission Tomography image reconstruction for robust, accurate and efficient imaging. This project timely addresses industry needs with novel solutions and will establish a technology roadmap to inform and .... Artificial intelligence empowered multi-modal biomedical imaging. This Industry Fellowship aims to transform biomedical imaging using artificial intelligence with world-leading industry partners. The project expects to make a major advance in multi-modal Magnetic Resonance Imaging and Positron Emission Tomography image reconstruction for robust, accurate and efficient imaging. This project timely addresses industry needs with novel solutions and will establish a technology roadmap to inform and de-risk future research and development in image reconstruction. The project outcomes should provide benefits to Australians with cost-effective imaging and benefits to Australia's biomedical industry with well-aligned intellectual properties and training of future scientists with industry knowledge.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240102254

    Funder
    Australian Research Council
    Funding Amount
    $399,132.00
    Summary
    Creating a non-invasive window into the mind. This project aims to create better tools to study the human mind. This project expects to generate new knowledge that can be used to non-invasively image neuronal activity. Expected outcomes include the development of unique new Magnetic Resonance Imaging (MRI) instruments to study neuronal activity in both highly controlled laboratory conditions and in humans, with the spatial and temporal resolution needed to study the neuronal circuitry that drive .... Creating a non-invasive window into the mind. This project aims to create better tools to study the human mind. This project expects to generate new knowledge that can be used to non-invasively image neuronal activity. Expected outcomes include the development of unique new Magnetic Resonance Imaging (MRI) instruments to study neuronal activity in both highly controlled laboratory conditions and in humans, with the spatial and temporal resolution needed to study the neuronal circuitry that drives low and high-level brain functions, i.e., creating a window into the mind. In the future, outcomes from this study could improve our understanding of mental disorders, advance computer brain interface technology, and inspire the next paradigm shift in artificial intelligence.
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