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Socio-Economic Objective : Expanding Knowledge in Engineering
Field of Research : Image Processing
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  • Researchers (34)
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  • Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE210101297

    Funder
    Australian Research Council
    Funding Amount
    $429,000.00
    Summary
    A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the e .... A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the emerging field of biological imaging and to deliver an integrated imaging platform for mapping various tissue microscopic components at the cellular level. Successful outcomes have the potential for commercialisation and will accelerate a range of fundamental science and engineering studies requiring imaging techniques.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT110100623

    Funder
    Australian Research Council
    Funding Amount
    $771,208.00
    Summary
    Multi-modal virtual microscopy for quantitative diagnostic pathology. This project will contribute to the next generation of virtual microscopy systems that provide innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of diagnostic pathology. These tools will especially contribute to the screening and diagnosis of cervical, lung and bladder cancer.
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    Funded Activity

    Discovery Projects - Grant ID: DP180101257

    Funder
    Australian Research Council
    Funding Amount
    $462,080.00
    Summary
    Dielectric contrast imaging for 7 Tesla magnetic resonance applications. This project aims to develop novel radio-frequency (RF) technology, ensuring that the benefits of high-field magnetic resonance imaging (MRI) are available for a broader range of applications. This project will develop a new contrast mechanism directly related to the RF properties of individual tissue types, circumventing a limitation of intensity based imaging. This technology will enhance Australia’s global impact the dev .... Dielectric contrast imaging for 7 Tesla magnetic resonance applications. This project aims to develop novel radio-frequency (RF) technology, ensuring that the benefits of high-field magnetic resonance imaging (MRI) are available for a broader range of applications. This project will develop a new contrast mechanism directly related to the RF properties of individual tissue types, circumventing a limitation of intensity based imaging. This technology will enhance Australia’s global impact the development of imaging technology for healthcare, biomedical research and advanced diagnostics.
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    Funded Activity

    Discovery Projects - Grant ID: DP140102794

    Funder
    Australian Research Council
    Funding Amount
    $295,000.00
    Summary
    Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t .... Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.
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    Funded Activity

    Discovery Projects - Grant ID: DP140100408

    Funder
    Australian Research Council
    Funding Amount
    $320,000.00
    Summary
    Advanced Magnetic Resonance Imaging at 7 Tesla: Resolving the fundamental radiofrequency field-tissue interaction problem at ultra-high field. Ultra-high-field Magnetic Resonance Imaging (MRI) systems offer the potential for faster, more accurate diagnostic imaging. However, current applications are limited by the fundamental challenge of strong interactions between the electromagnetic field and human tissues, which result in poor image quality and/or compromised patient safety. Using a novel, s .... Advanced Magnetic Resonance Imaging at 7 Tesla: Resolving the fundamental radiofrequency field-tissue interaction problem at ultra-high field. Ultra-high-field Magnetic Resonance Imaging (MRI) systems offer the potential for faster, more accurate diagnostic imaging. However, current applications are limited by the fundamental challenge of strong interactions between the electromagnetic field and human tissues, which result in poor image quality and/or compromised patient safety. Using a novel, subject-specific imaging approach, this research will design and develop an ultra-high-field radiofrequency technology capable of offering high-performance imaging without jeopardising patient safety. This research will lay the groundwork for the translation of ultra-high field MRI research into clinical practice, generating new capabilities for diagnostic technologies.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP210200594

    Funder
    Australian Research Council
    Funding Amount
    $885,000.00
    Summary
    The worlds next door: terrestrial exoplanets with the TOLIMAN space mission. This project aims to to explore our nearest neighbour star system, Alpha Centauri, for the first time probing for exoplanets with physical characteristics that resemble those of Earth. The finding of any such world, with the potential to support a biosphere like our own and lying only 4 light-years away, would profoundly alter our view of our place in the universe. The primary outcome of this project will be the design, .... The worlds next door: terrestrial exoplanets with the TOLIMAN space mission. This project aims to to explore our nearest neighbour star system, Alpha Centauri, for the first time probing for exoplanets with physical characteristics that resemble those of Earth. The finding of any such world, with the potential to support a biosphere like our own and lying only 4 light-years away, would profoundly alter our view of our place in the universe. The primary outcome of this project will be the design, construction, launch and operation of a novel and innovative space telescope: the TOLIMAN mission. This profoundly benefits the Australian space and university sectors, partnering them with international agencies to deliver marquee science with global impact: the search for our first stepping stone to interstellar space.
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    Funded Activity

    Discovery Projects - Grant ID: DP130100202

    Funder
    Australian Research Council
    Funding Amount
    $780,000.00
    Summary
    Real-time cardiac magnetic resonance imaging: a compressed-sensing framework incorporating sensor design and multidimensional signal reconstruction. Conventional Magnetic Resonance Imaging (MRI) technology is fundamentally constrained by slow scan speeds. Taking a new approach to cardiac imaging - which integrates MRI hardware design with a novel dynamic imaging method based on compressed sensing - this project enables faster and more accurate dynamic imaging for the diagnosis of heart disease.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT130101394

    Funder
    Australian Research Council
    Funding Amount
    $606,740.00
    Summary
    Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi .... Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.
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    Funded Activity

    Discovery Projects - Grant ID: DP110102888

    Funder
    Australian Research Council
    Funding Amount
    $255,000.00
    Summary
    Foundations and advanced algorithms for topological image processing. Building on new links between the mathematical discipline of homology and digital images, this project develops a new class of topology-driven image analysis techniques that will improve the accuracy and reliability of predictions made from the powerful new generation of three dimensional microscopes.
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    Funded Activity

    Discovery Projects - Grant ID: DP150104279

    Funder
    Australian Research Council
    Funding Amount
    $495,900.00
    Summary
    Enhanced Through-Wall Imaging using Bayesian Compressive Sensing. The aim of this project is to develop radar imaging techniques which enable us to 'see' objects behind walls and opaque materials. The major intended breakthrough is the ability to image objects behind walls and inside buildings or enclosed structures without accessing the scene. Novel signal and image processing algorithms, based on Bayesian compressive sensing, will be developed to enhance image quality and resolution, improve s .... Enhanced Through-Wall Imaging using Bayesian Compressive Sensing. The aim of this project is to develop radar imaging techniques which enable us to 'see' objects behind walls and opaque materials. The major intended breakthrough is the ability to image objects behind walls and inside buildings or enclosed structures without accessing the scene. Novel signal and image processing algorithms, based on Bayesian compressive sensing, will be developed to enhance image quality and resolution, improve speed of operation, and reduce the cost and time of data acquisition and processing. Many applications are expected to benefit from this research including search and rescue, surveillance, security, and defence. The research outcomes are expected to enhance the capabilities of the Australian armed forces, counter-terrorism, police and law-enforcement agencies.
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