Medical Imaging

EPSRC Centre for Doctoral Training

Research

Projects

  • 108: Free-breathing assessment of myocardial fibrosis at a microstructural level using spin-echo cardiac diffusion tensor imaging

    The aim of the project is to develop a robust and reproducible free-breathing MRI technique for the assessment of the microstructure of replacement and diffuse fibrosis in the heart. More...

  • 210: A Pretargeted PET imaging Strategy for Nanomedicines – A theranostic tool

    In this project we will synthesise a series of novel compounds and develop methods that will allow us, for the first time, to radiolabel liposomal nanomedicines with unchelated Ga-68 in vivo. This will represent a simple, low-radiation and convenient tool for the non-invasive long-term monitoring and quantification of nanomedicinal biodistribution at any given time. In addition, we will use this technique to demonstrate the efficacy of high-intensity focused ultrasound to improve the accumulation of these nanomedicines in target tissues. More...

  • 301 - The Automated Neuroscientist a Bayesian Optimisation Approach

    An ongoing challenge in cognitive neuroscience is developing novel methods to explore the link between neural activity and cognition. In traditional functional MRI (fMRI) experiments, subjects are scanned while they perform specific tasks (e.g. a memory or visual task) to find out which brain regions the tasks are associated with. In this PhD project, we will develop a novel approach, the “automated neuroscientist”, which turns the typical fMRI design on its head: we will develop novel computational techniques for real-time fMRI to track brain activity, and identify a set of tasks/stimuli that activate a specific target neural state of interest, such as a brain connectivity network. State-of-the-art Bayesian optimisation algorithms will be developed to explore a large bank of pre-defined tasks in real-time by sequentially processing the acquired fMRI data. Potential clinical applications will be explored by assessing individual differences in cognitive dysfunction following focal stroke. More...

  • 302 - Explanatory cardiac motion models based on machine learning

    The motion and deformation of the heart as it beats can provide valuable information about cardiac health. In recent years techniques based on spatiotemporal atlases have emerged that have enabled disease to be diagnosed and characterised from image-derived measurements of motion and deformation. Such atlases establish a common space where the motions/deformations of different subjects can be compared, and machine learning approaches can be subsequently used for classification and regression. However, like most machine learning algorithms, whilst producing highly promising results in terms of accuracy, these techniques typically lack explanatory power. For example, they can tell you what disease is present but they cannot explain in simple terms how they reached that decision. Recent work in the machine learning literature is seeking to overcome this weakness. This project aims to build upon these recent developments to produce a computer-aided diagnosis tool that can analyse cardiac motion and deformation and provide interpretable explanations as well as accurate characterisations of disease. More...

  • 306 - Modality propagation and machine learning for detection of the epileptogenic zone from [18F]FDG PET and MR imaging

    Investigate the potential of modality propagation and machine learning for the objective detection of the epileptogenic zone during the pre-surgical workup of patients with refractory focal epilepsy. More...

  • 312: Is endothelial function assessment feasible by non-invasive pulse wave analysis? A computational and in vivo study

    Dysfunction of endothelial cells lining blood vessels is a major risk factor for cardiovascular disease. The aim of the project is to investigate whether endothelial function can be assessed from pulse waves measured non-invasively. This will be achieved by studying haemodynamic mechanisms underlying changes in the shape of peripheral pulse waves produced by pharmacological drugs that alter endothelium function and by determining whether endothelium dysfunction alone is responsible for significant variations in the contour of pulse waves. The research will be carried out using computational modelling of blood flow in the larger systemic arteries of both the rabbit and human. Rabbit data is valuable since a complete anatomic and haemodynamic dataset is available to calibrate and validate numerical models and, hence, study the problem in detail. This research is necessary before non-invasive, cost-effective diagnosis of endothelial dysfunction based on pulse waves could enter routine use. More...

  • 315: Computational Modelling Cellular Variability

    Robust cardiac function depends on the billions of individual cells that make up the heart working together to contract synchronously and pump blood. While all cardiac cells are similar, experimental measurements show that each cell is also distinct. Conventionally experimental and clinical recordings are averaged to remove variability from measurements, however, this approach removes the measurement of potentially important physiological or pathological co-variation and variation. Combining advanced multi-scale biophysical computer simulations of cardiac function (Figure 1) with Bayesian statistical approaches, this PhD will develop a statistical-systems approach to quantify the degree and impact of variability and covariance in clinical and experimental measurements. More...

  • 316: The Connectome Avatar Project

    The project aims to embody our current generation of computational brain models, that are based on the anatomical Human Brain Connectome, into novel sensory-motor simulated Avatars. More...