Smart Medical Imaging

EPSRC Centre for Doctoral Training


Current Projects

  • Megan Midson
  • Casper da Costa-Luis
  • Saul Cooper
  • Giorgia Milotta
  • Marina Strocchi
  • Rob Robinson

    Automated Quality Control and Semantic Parsing in Multi-modal Imaging

    Rob Robinson - 2015 entry

    The aim of this project is to develop computerised methods for automatic quality control (QC) in large-scale imaging studies, such as in late-phase pharmaceutical development. QC is an important aspect in imaging which ensures that the acquired data is useful for the intended purpose. The process of QC aims to make sure that the correct anatomical structures are imaged and fully visible, that the image quality is sufficient, for example, for diagnostic purposes, and that the data is not corrupted by any artefacts (which can have a severe impact on any detailed computational analysis). Efficient and prompt QC can be a vital step in ensuring a patient’s safety or eligibility for a clinical study. Additionally, QC is essential for regulatory purposes and clearance of novel approaches to treatment and intervention. More...

  • Jessica Dafflon

    Biologically interpretable models for brain disorders

    Jessica Dafflon - 2015 entry

    Increasingly complex dynamics of the human brain are being investigated. However, it is unclear how these complex dynamics modulated or relate to neurobiology. We aim to develop computational models for multimodal neuroimaging data to explore changes in complex dynamics as a result of pharmacological intervention or in disease states. These novel metrics maybe potentially useful both to examine hypotheses of disease processes and potentially relevant clinical biomarkers. The candidate will explore a range of generative computational models at various levels of complexity that predict functional connectivity (evaluated using fMRI from the human connectome project (HCP), KCL and ICL) and explore neurobiologically relevant constraints such as structural connectivity (DTI from the HCP) and neurotransmitter distributions (PET from KCL and ICL). The student will also evaluate the potential of these biomarkers in terms of predicting clinical outcome (eg using the parameters of trained computational model as input to a classifier, ie ‘generative embedding’). More...

  • Peter Gawne

    Cell and liposome tracking by PET with zirconium-89

    Peter Gawne - 2015 entry

    Standard radiolabelling methodology for cell tracking by scintigraphy has exploited non-specific assimilation of lipophilic, metastable complexes of indium-111 (eg. with oxine). New developments in cellular medicine are creating new applications for cell tracking in humans, including some with small lesions/cell numbers below the sensitivity of SPECT with 111In (eg coronary artery disease, diabetes, neurovascular inflammation and thrombus), creating a need for positron-emitting analogues to bring the benefits of PET to cell tracking. The first feasible solution to this, using Zr-89, has been developed by the Blower group and has shown great promise in early preclinical in vivo evaluation. In addition, Torres has shown that these complexes are able to radiolabel liposome drug carriers by similar mechanisms. This project will optimise the design and use of the labelling compound and evaluate the effects on radiobiology and survival of the labelled cells and their in vivo behaviour, as a preparation for clinical application in imaging inflammation in diseases such as atherosclerosis. We will also use the optimised tracers for labelling of drug carrying liposomes to track their fate and drug delivery in vivo. 89Zr is a long half-life positron emitter that could meet this need. More...

  • Ksenia Grozdova

    Computational modelling of dynamic brain connecting networks for disease prediction

    Ksenia Grozdova - 2015 entry

    Alteration in brain connectivity, as captured for instance by functional MR imaging, has been found to be associated with a variety of clinical disorders. However only a handful of suitable statistical models and machine learning techniques for predicting a disease status or other clinical outcome from brain networks have been developed so far. The aim of this project is two-fold: (a) to develop statistical models for the estimation of time-varying brain connectivity networks from functional MRI data, which do not pose the unrealistic assumption of time series stationarity, and (b) to develop machine learning techniques for the analysis of time-varying networks with the purpose of extracting connectivity patterns that are highly predictive of a clinical outcome. The resulting methods and tools will be tested and validated on a number of publicly available data sets, such as those generated by the Human Connectome Project (HCP) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). More...

  • Rhiannon Evans

    Developing new targeted molecular contrast agents for imaging inflammation of vulnerable plaques

    Rhiannon Evans - 2015 entry

    This PhD project is uniquely situated between chemistry (ICL-Chem), bioengineering (ICL-Bioeng) and imaging (KCL-Imag), and aims are divided according to departments. Aim 1: Development of a bespoke nanotechnology platform for optimal imaging of vulnerable plaques (ICL-Chem). Aim 2: Identification of new biological targets for vulnerable plaques detection on the basis of recently performed genome-wide screening of a unique animal model (ICL-Bioeng). Aim 3: Testing of the new targets in murine and pig tissue with MRI/PET/CT. We will test (i) the passive uptake, (ii) distribution and (iii) active and specific uptake of the molecular contrast agents in vulnerable plaques (KCL-Imag). More...

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