Medical Imaging

EPSRC Centre for Doctoral Training

Students

Current Projects

  • James Bezer

    High-Resolution Contrast-Enhanced Ultrasound Elastography

    James Bezer - 2017 entry

    Mechanical forces – the pulsing of blood, pumping of the heart, stretching and grabbing of immune cells – govern the daily life of our bodies. When the mechanics of a cell or tissue go awry, they give early clues to a developing disease – changes in blood flow have been linked to atherosclerosis and changes in stiffness have been linked to cancer metastasis. Catching these signs early could save lives by getting the right treatments to the patient before the disease becomes unmanageable. Recent developments in ultrasound and magnetic resonance imaging have demonstrated the ability to image tissue elasticity – also known as elastography. However, these methods can neither resolve very small regions of changes nor very small changes in stiffness, and so the vast majority of diseases with known changes in mechanical properties remain undiagnosable. The purpose of this PhD project is to overcome these resolution and contrast limits by developing the world’s first contrast-enhanced ultrasound elastography system. More...

  • Rian Hendley

    Imaging and sensing in living cells using dual modality fluorescent PET imaging agents

    Rian Hendley - 2017 entry

    Positron Emission Tomography (PET) is a powerful technique, used particularly in oncology, which allows three-dimensional imaging of tissue deep in the body (2 million scans in the US each year). However, substantial infrastructure is required for (often short-lived) radioisotope generation. Incorporating fluorescence within the same agent allows imaging through the emission of visible light to indicate the location of the agent. Adding targeting units to the probe ensures high selectivity for tumours, thus creating a targeted, dual modality agent for the imaging of cancer. Importantly, this will allow visualisation of the tumour site before an invasive procedure (using PET) and, once radiation is no longer present, during surgery (using the fluorescence). The inbuilt flexibility of the system proposed will allow many different types of tumours to be targeted selectively using different targeting groups attached to the metal centre. This will enhance the potential for clinical translation and future commercial development. More...

  • Matthew Farleigh
  • Krishna Seegoolam

    Learning optimal image representations for MRI reconstruction, synthesis and analysis

    Krishna Seegoolam - 2017 entry

    The aim of the project is to develop machine learning approaches for the reconstruction, synthesis and analysis of MR images. In particular, we aim to develop approaches in which image acquisition, reconstruction and analysis are not carried out in a sequential fashion, but are integrated into a joint reconstruction and analysis framework. This will enable feedback between the image acquisition, reconstruction and analysis stages, leading to improved reconstruction as well as analysis. The project will address the following challenges: • How can we learn image representations that can serve as effective and informative priors for image acquisition/reconstruction? • How can we learn image representations that can be used for image synthesis/analysis tasks, e.g. image super-resolution or image segmentation? • How can these image representations be combined for joint reconstruction and analysis? More...

  • Fraser Edgar

    Nanoscale microfluidic reactions: ‘near-stoichiometric’ radiolabeling for PET

    Fraser Edgar - 2017 entry

    The aim of this project is to develop the concept of near-stoichiometric C-11 radiolabeling using a droplet based microfluidic reactor that will enable the rapid synthesis and purification of PET tracers. More...

  • Emily Chan
  • Alex Rigby

    Radiobiological assessment of radionuclide pairs used in theranostic (imaging and therapy) approaches

    Alex Rigby - 2017 entry

    Molecular imaging with radionuclides can pinpoint disease locations and measure changes in metabolism and gene expression, and the same molecular targeting can deliver effective radionuclide therapy to cancers. The biological effects of the radionuclides, however, remain poorly understood. This project will investigate small and large scale biological effects of radionuclides used in imaging (diagnosis and monitoring therapy response) and therapy (killing cancer cells) to better understand how radionuclides kill cells, making radionuclide therapies more effective and imaging tracers safer. A range of important medical radionuclides will be tested for their intended and unintended effects on DNA and chromosomal damage, induction of DNA repair pathways and cell kill, in vitro and in vivo. This in turn will provide the ideal basis to determine whether current imaging modalities used in the clinic can be used more frequently and could enhance the way in which patients are treated with radiopharmaceuticals. More...

  • Jonathan Jackson

    Real-time assessment of coronary haemodynamics via hybrid fluid dynamics & machine learning approach

    Jonathan Jackson - 2017 entry

    The aim of the project is to develop a non-invasive computational diagnosis pipeline for assessing the severity of coronary lesions that could easily be applied under real-time interventional settings. To achieve this, a combination of computational fluid dynamics and machine learning techniques will be used. As the full-scale CFD modelling is time-consuming, requires expertise not widely available in clinical environments, and is unsuitable for direct implementation into cathlab consoles, in this project we will seek to develop a reduced-complexity model for which the lumped shape parameters are estimated directly from the medical images using statistical learning techniques. The developed framework will be applied to 200+ clinical cases for validation, and evaluated in a pilot study in the final year of the project. More...

  • Elisa Roccia
  • Cian Scannell

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