Medical Imaging Analytics

An interdisciplinary approach in which engineering technologies and a combination of computational and dynamic techniques are used to solve real world clinical and industrial problems in the field of vascular flow.

Developing and applying novel Magnetic Resonance Imaging and ultrasound imaging and analysis methods across a range of clinical disorders to quantify tissue mechanical properties, tissue deformation, and fluid flows. These methods are valuable in understanding disease mechanisms, and in providing information for diagnosis and monitoring of disease progression.

Identifying and classifying the risk of progression in age-related macular degeneration (AMD) by applying pattern recognition to multi-modal images of the retina, changes in which can also be used to diagnose and analyse the progression of other retinal and optic nerve diseases.

Using data analytics, machine learning and deep learning techniques across clinical and imaging datasets to provide the opportunity for establishing personalised medicine approaches to cancer treatment.

Research and development of quantitative methods focused on understanding how cytotoxic immune cells can be harnessed for the treatment of solid malignancies. Specialise in revealing both the biological and mechanical processes that underpin the search, recognition and elimination phases of killer immune cell-mediated tumour rejection.

Developing novel methods of biomedical diagnostics using hyperspectral microscopy to characterise natural colour and morphology of cells and tissues in the body, to determine whether they carry the early hallmarks of disease. This can yield early screening systems for detecting ill but pre-symptomatic individuals.

Incorporating novel imaging technologies with correlative approaches, image rendering and interaction capabilities using geospatial tools, as well as image analytics that incorporate machine learning, to understand cellular epidemiology of disease and develop novel next generation diagnostics.

A unique integration of radiotherapy with real-time MRI-based image guidance that allows cancer and normal tissues to be seen directly during radiotherapy for the first time. Approximately 50% of cancer patients will benefit from such a system that will improve survival, prevent recurrence or relieve the symptoms of their cancer.

Advanced computational methods for automated biomedical image analysis, health informatics and downstream data analytics to improve the reliability and throughput of imaging-and multimodal data-based diagnostics and screening.

Research Imaging NSW (RINSW) is a new strategic initiative developed in partnership between UNSW, South East Sydney Local Health District and Neuroscience Research Australia to provide state-of-the-art MR imaging capabilities, and increase collaboration between leading academic, research and health care institutions.

Advanced flow modelling and experimental analysis from medical images, virtualisation and simulation for structural and fluid dynamic investigation. This includes populational statistics and data-driven mapping for biomarker detection, improved diagnostics and treatment optimisation.

Any minimal trauma fracture (MTF) doubles the risk of future fractures. XRAIT uses natural language processing of radiology reports to allow risk stratification of patients by clinical services to reduce the social and economic impact of osteoporosis and ensure that their first fracture is their last.

The application of clustering and separability statistics on topographic data from the anterior eye assists in the classification of corneal disease or the identification of those at risk of angle-closure glaucoma. Imaging data from routinely available clinical instruments may be used and thus the method is transferable across existing imaging platforms.

A research group comprised of perinatal clinicians and biomedical engineers who conduct translational imaging research in a hospital setting. The multidisciplinary team allows development of novel ultrasound algorithms to image, evaluate and quantify structure and function (perfusion and impedance) of structures such as organs and tumours.

Correlative light and electron microscopy (CLEM) is a synergistic microscopy approach for obtaining structure and function information from single cells and tissue samples. Correlation is obtained by overlay on ultrastructure of either biochemical information from other microscopy modalities such as immunocytochemistry, MRI or analytical platforms such as imaging mass spectrometry.

This public health research group has a demonstrated ability to assemble multi-disciplinary teams to address priority issues in injury prevention and eye health, innovate and build capacity in this sector.