Medical Imaging

EPSRC Centre for Doctoral Training

Research

Projects

  • 118 - Passive Imaging of Acoustic Sources: Novel imaging algorithms for monitoring therapeutic ultrasound

    Acoustic sources are objects that emit sound, such as a car, a dolphin, and underground rivers. Sound emitted from these sources can be captured to identify them or characterise their behaviours. Acoustic detection is unique, because it can describe objects through opaque material. One limitation with acoustic detection methods was that they are unable to locate – with good precision – where the sound is coming from. More...

  • 209 - The development of molecularly imprinted polymer nanoparticles for PET agent delivery and boron neutron capture therapy

    PET scanning requires delivery of radioactive tracers to sites of interest such as neuroreceptors and malignant tumours. Radiotracers incorporate radioactive isotopes with short half-lives; fluorine-18 is the most widely used, with a half-life of approximately 110 minutes. Delivery of these isotopes currently involves incorporating them into compounds that are readily processed by the body, then observing sites with build-up of 18F. More...

  • 212 - New Lipophilic Cations for Imaging Apoptosis and the Mitochondria

    Apoptosis is the most common form of programmed cell death and is a key mechanism in many pathological diseases. These include cancer, diabetes, neurodegenerative disorders and aging. Being able to fully understand this mechanism could lead to huge advances in detection, drug development and treatment. Currently there are very few non-invasive techniques capable of quantifying and assessing the process of apoptosis in humans. The discovery that mitochondria play an important role in the early stages of apoptosis has directed focus to targeting the mitochondria as a means of identifying disease. Changes in mitochondrial membrane potential (m) can be directly related to mitochondrial dysfunction, representing a biophysical process that could be targeted with imaging. More...

  • 301 - Explaining the predictions of deep learning models in cardiology

    The use of machine learning techniques in cardiology is an active research area and recently state-of-the-art results have been achieved by applying the latest ‘deep learning’ techniques to tasks such as segmentation of the myocardium and blood pool [1]. Machine learning has also been used to identify and exploit the useful information carried by the motion and deformation of the heart as it beats [2,3]. More...

  • 304 - Non-invasive assessment of pulmonary hypertension using imaging and pulse wave analysis

    PH is a life-threatening condition of the pulmonary circulation clinically defined by a sustained elevation of blood pressure in the pulmonary arteries. The main vascular changes in the initiation and progression of PH are vasoconstriction, remodelling, increased arterial stiffness, thrombosis, and vascular cell proliferation. These homeostatic imbalances produce profound haemodynamic changes, including a substantial increase in pulmonary vascular resistance, reduction of vessel distensibility (flexibility), and reduction of cardiac function, ultimately leading to right ventricular failure and death.1 More...

  • 306 - Developing an fMRI-diagnostic framework for characterising cognitive control impairments based on machine learning analyses of dynamic network states

    Recent neuroimaging research has demonstrated that the brain has a highly adaptable functional network structure. Different networks are specialised to different functions, but the diversity of tasks that we face in everyday life can tap these functions in different combinations. This leads to the observations of ‘dynamic network states’. Simply put, the vast array of possible tasks that we can perform is reflected by the diverse conjunctions of network configurations that the brain can transiently express. These are visible to fMRI as transient patterns of correlated activity across brain regions. More...

  • 307 - Development of a decision support tool for neuroimaging using explainable ML

    The purpose of this project is to develop an ML-based decision support tool for brain Magnetic Resonance Images (MRI) with application to neurological disorders. A particular focus will be on the development of a decision support tool that will not only be able to diagnose patients based on clinical imaging and non-imaging information, but will also be able explain how it has reached its decision. For this, we will use a large database of multi-modal brain MRI from patients with different forms of dementia. More...