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.Read moreRead less
Ultra-fast serialised all optical image processing: addressing the electronic bottleneck in the world's fastest camera. Serial time encoded amplified microscopy can capture over a million frames per second. At this rate, a megapixel image would fill a terabyte hard disk in a second. We will use photonics to condense and manipulated the video stream so that only the important features are 'seen', making it practical to process and store on a computer.
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.Read moreRead less
Novel technologies for motion-compensated simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) imaging. The aim of this work is to develop motion tracking and motion correction techniques for an emerging hybrid imaging technology, MR-PET. The MR-PET scanner simultaneously acquires structural MR images and functional PET images. The work will provide clearer images without the effects of motion blur for both research and clinical applications.
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less