Medical Imaging

EPSRC Centre for Doctoral Training


Current Projects

  • Aishwarya Mishra
  • Daniel Grzech

    Automated assessment of fetal movements as an indicator of postnatal neurological health and function

    Daniel Grzech - MRes

    Neonatal movements are commonly assessed as a way of predicting neurological function of the baby, but methods currently in use are subjective, time-consuming and are not standardised. It has been shown that there is continuity between fetal and neonatal movement patterns, and that fetal movements are affected by certain neurological disorders, but fetal movement patterns are not routinely assessed. This project will develop a system for automatically tracking and characterising fetal movements visualised using cine MRI. A large bank of fetal movement data has already been gathered for normal subjects and for subjects at increased risk of neurological conditions, e.g., ventriculomegaly (enlarged lateral ventricle in the brain). From this data, this project will identify one or more movement-based ‘biomarkers’ indicative of neurological function using image-based feature identification and tracking methods combined with machine learning approaches. This project forms part of a platform approach to objective assessment of fetal and neonatal movement patterns for early diagnosis of neurological abnormalities. More...

  • Hugh O'Brien

    Comprehensive CRT CT Imaging (3CI)

    Hugh O'Brien - MRes

    This project will aim to exploit recent advances in dual energy CT scanners that provide high image contrast and with low radiation dose cardiac imaging at exceptional spatial and improved temporal resolution. The objective of this PhD will be the design, development and testing of an image processing and visualisation platform for extracting key indices of cardiac function and anatomy from cardiac CT imaging to predict the outcome of CRT and guide therapies. More...

  • Vassilis Baltatzis

    Deep learning for early detection of lung cancer in patients at risk

    Vassilis Baltatzis - MRes

    The project aim is to explore and develop novel machine learning approaches based on ‘deep learning’, as applied to serial low-dose lung CT imaging for early lung cancer identification in high-risk cohorts. Early identification is challenging because symptoms are non-specific (or absent), compounded by overlap with symptoms of chronic obstructive pulmonary disease (COPD). Early diagnosis using CT relies on the detection of lung nodules and an accurate evaluation of their growth. However, manual radiological assessment is problematic because of inter-observer and inter-scan variability. This project will address the key medical imaging challenges arising from co-existent emphysema, inter-current infection, differing levels of inspiratory effort and variable acquisition parameters in patients with CT-detected nodules, by devising novel solutions using machine learning methods belonging to the class of deep learning architectures, an emerging and particularly promising area of medical image analysis, in order to detect lung cancer at an early stage. More...

  • George Firth

    Development of tracers for in vivo trafficking of essential trace metals in health and disease using PET imaging

    George Firth - MRes

    The aim is to develop methodology for production of radionuclides and tracers to support a programme of study on use of PET to help understand changes in trafficking of essential trace metals (Zn, Cu, Mn, Fe) in diseases where they are highly relevant, such as diabetes, cancer, dementia and pulmonary hypertension. The objectives will be (a) to set up Zn-63 production at KCL (Cu-64/62 already implemented at KCL; Fe-52 and Mn-52 purchased externally; Zn-62 production already in place); (b) develop suitable delivery vehicles e.g metastable lipophilic complexes, ionic salts, protein bound (albumin, transferrin)and routes to control trafficking of the metals via endogenous transport, retention and excretion mechanisms; (c) use them to map normal trafficking of Cu, Zn, Fe, Mn in mice, (d) map changes in trafficking in disease models, and (e) identify translational opportunities for human PET studies More...

  • Caitlin Hardie

    Fetal quantitative flow measurement by MRI

    Caitlin Hardie - MRes

    The aim of this project is to develop and validate q-flow methods for assessing the fetal cardiovascular system and cerebral circulation using an image domain approach with full motion correction based on kt-sampling, novel reconstruction and image registration methods. More...

  • Carlos Cueto Mondejar

    High resolution and high contrast imaging and quantification of cardiac macro- and micro- blood flow based on ultrafast ultrasound acquisition and signal processing

    Carlos Cueto Mondejar - MRes

    The aim of this project is to develop and evaluate the next generation echocardiography techniques for accurate and early diagnosis of e.g. coronary artery diseases and thrombus, taking advantage of ultrafast ultrasound acquisition (kHz frame rate), microbubble contrast agents and signal processing. More...

  • David Leitao

    High resolution imaging of tissue properties with optimized precision using Ultra High Field MRI

    David Leitao - MRes

    The Aim of the project is to design a new quantitative MRI approach for Ultra-High Field (UHF) MRI. UHF-MRI brings with it the promise of enhanced signal-to-noise (SNR) leading to very high resolution imaging. However UHF-MRI is restricted by stringent constraints on radio-frequency (RF) power absorption and hardware limitations, as well as highly spatially non-uniform RF fields. The project will investigate approaches for optimizing the efficiency of quantitative MRI (qMRI) sequences in the context of UHF-MRI, with the aim of producing robust T1/T2 parameter maps within the shortest possible amount of time. More...

  • James Bezer
  • Rian Hendley

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

    Rian Hendley - MRes

    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...

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