Imaging of heart


Sarah Essilfie-Quaye

10 June 2018

The National Heart and Lung Institute has funding available for an Engineering and Physical Sciences Research Council (EPSRC) PhD Studentship to start in October 2018.  

Our projects fulfil the EPSRC research aims while delivering the world leading medical research associated with our Faculty.  Successful candidates may have a mathematics, physics or engineering background or area of specialism as well as in interest in biomedical sciences.

The studentship will be for 3.5 years, so as to include a funded 6-month writing-up period. An enhanced stipend of £18,000 p.a. is offered, together with a Conference Fund of £300 p.a. and fully funded tuition fees at the home rate. Successful students will become part of the Imperial MRC DTP cohort and benefit from the network and support provided for these students.

Choose a project from those listed below:

Project 1 - Engineering new non-viral vectors for gene transfer and genome editing

Primary Supervisor: Eric Alton
Co-supervisors: Uta Griesenbach, A Patel

We have recently shown that non-viral gene therapy can stabilise lung disease in cystic fibrosis patients, but the effect was comparatively modest. As part of this studentship, we will develop more potent, improved non-viral gene transfer agents for respiratory gene therapy through addition and modification of bioactive components. Importantly, non-viral vectors are also gaining significant interest in the context of genome editing. We envisage that this PhD will work across several Imperial College Faculties and significantly contribute to the development of advanced therapeutic investigational medicines.

Project 2 - Developing pH sensing semiconductors for point-of-care detection of emerging antifungal drug resistance

Primary SupervisorDarius Armstrong-James
Co-supervisorsMatt FisherPantelis Georgiou

Fungal antimicrobial resistance is on the increase owing to the dual-use of triazole antifungal drugs in agribusiness as well as medical settings. This is leading to an increase in the incidence of azole resistance mutations in Aspergillus fumigatus. Successful treatment of these patients would be greatly increased by understanding the pathways linking aerosolised environmental reservoirs to infection and developing rapid point-of-care diagnostics for antifungal mutations.  The student will develop isothermal DNA amplification techniques using microfluidic and novel ISFET-based CMOS arrays to detect antifungal mutations. These diagnostics will then be integrated into a static airsampler to monitor temporal exposure of patients to resistant A. fumigatus. These diagnostics will then be tested at point-of-care in key Brompton patient cohorts (cystic fibrosis, bronchiectasis). Currently the prevalence of antifungal resistance in our cohort is 13% overall. This multifaceted project will train an individual with a multidisciplinary skillset applicable to environmental exposure and human health.

Project 3 - Mathematical modelling of neutrophilic inflammation in the airway during fungal exposure

Primary SupervisorDarius Armstrong-James
Co-supervisorReiko Tanaka

Neutrophilic inflammation in the airway is a key feature of a range of chronic respiratory diseases including asthma endotypes and cystic fibrosis. Increasing evidence has implicated inhalation of fungi such as Aspergillus fumigatus in the aerial mycobiota as a key factor in progression of chronic respiratory disease. We have developed a mathematical model of leukocyte dynamics in the airway during airway infection with Aspergillus fumigatus (Tanaka et al, 2015 Scientific Reports) and have subsequently developed a model of airway macrophage-neutrophil interactions during pulmonary aspergillosis (manuscript in preparation). Further lab-based studies have revealed that excessive neutrophilic inflammation in animal models of cystic fibrosis-related aspergillosis is linked to hyperinflammatory airway macrophage responses to Aspergillus fumigatus. The proposed project aims to achieve a mechanistic understanding of cystic fibrosis-related neutrophilic inflammation with wider applicability to other chronic respiratory disease states and related infections, via interactive mathematical modelling, machine learning data analysis and biological experimentation.

Project 4 - Development of a physiologically relevant model of the upper airway for the study of the intra and inter cellular interactions in asthma

Development of a physiologically relevant three-layer model of the upper airway for the study of the intra- and inter-cellular interactions during airway remodelling in asthma

Primary Supervisor: Pankaj Bhavsar
Co and Assistant supervisorJulian JonesCharalambos Michaeloudes

A feature of asthma is airway remodelling, a pathophysiological process which results in an  irreversible alteration in the structure of the airway wall, entailing airway smooth muscle layer thickening, epithelial shedding, goblet cell hyperplasia and sub-epithelial fibrosis.  This process is thought to be driven by damage to the epithelial cell layer which, upon release of inflammatory mediators, and other yet-to-be-determined cellular crosstalk, affect the underlying mesenchyme.

  • Aim one: Develop material scaffolds for an in vitro, physiologically relevant, three-layer co-culture model of the upper airway by determining the appropriate surface chemistry, pore size and mechanical properties of the biodegradable scaffolds. This will enable native growth of individual cell types which form the airway wall in a way that will allow subsequent separation of the individual cell layers for functional analyses.
  • Aim two: Investigate the pathophysiology of airway remodelling by investigating the responses of the reconstituted airway to inflammatory mediators, pathogens, and drugs.

Project 5 - Deep learning–driven integrative analytics of multi-source biological data in cancer metastasis

Primary SupervisorVania Braga
Co-supervisorKirill Veselkov

Metastatic behaviour is associated with poor prognosis in the vast majority of cancers.  Yet, although migration of tumour cells has had much attention in the past, the mechanisms via which cells detach away from neighbouring cells to initiate invasion is poorly understood.  The project will address this bottleneck and perform a phenotypic analysis coupled with pathway-oriented deep learning approaches to identify biological processes that disrupt cell-cell contacts.  We have large “unrefined” datasets of RNAi screens targeting adhesion, cytoskeleton and signalling molecules that underpin strong cell-cell adhesion in normal and cancerous tissues. Using these datasets and knowledge bases of molecular pathways, a PhD student will develop a deep learning framework to identify patterns of disruption and the biological processes associated with each pattern. The long-term goal is that determining pathways perturbed in a particular cancer will help with personalised therapeutic approaches to ameliorate tumour progression.

Project 6 - Durable Efficient Lung Testing Apparatus for low income settings (DELTA)

Primary SupervisorPeter Burney
Co-supervisorPeter Childs

Chronic lung disease is a major problem in low income countries, and low lung volumes are a particular problem. These can only be assessed using spirometry or more complicated methods and a survey in 2009 showed that many chest physicians working in Africa did not have access to spirometry. Students in the Dyson School of Design Engineering have shown that a spirometer that was robust, reliable, biologically safe and easy to use could be designed at a fraction of the cost of current spirometers using the concept of the de Bono whistle. 

The student would complete the design of the spirometer and produce an interface that was easy to use by patients and healthcare personnel. The product would be developed and tested using the BOLD network. The engineering system would be supervised by Professor Childs (DE) and the technology assessment would be supervised by Professor Burney (NHLI).

Project 7 - Artificial Intelligence Approaches to Predict Advanced Coronary Atherosclerotic Plaque Development and Progression

Primary SupervisorRanil de Silva
Co-supervisor: Anil BharathDaniel Rueckert

Identifying the specific coronary narrowings (plaques) in patients which progress to cause heart attacks remains a major unmet clinical challenge. Our previous work suggests that the local biomechanical environment determines coronary atherosclerotic plaque formation, progression and rupture risk. Fluid-structure interaction (FSI) finite element modelling enables comprehensive evaluation of the biomechanical environment using standard clinical intracoronary imaging modalities. We will use the novel and emerging technologies of artificial intelligence (AI) to: i) automate image segmentation and 3D vessel reconstructions; ii) reduce the computational cost of FSI modelling (deep learning algorithms); and iii) to develop and train predictive models that can be clinically translated (supervised machine learning). This will be achieved using source data from longitudinal pre-clinical studies (BHF-funded). We will translate this work clinically to test if these trained models enable prediction of changes in coronary plaque morphology and composition thus providing personalised prediction of future heart attack risk in patients.

Project 8 - Diffusion Tensor Cardiovascular Magnetic Resonance

Primary SupervisorDavid Firmin
Co-supervisorsAndrew Scott

Diffusion Tensor (DT) Cardiovascular Magnetic Resonance (CMR) allows non-invasive interrogation of the 3D microstructural dynamics of the heart. Pilot clinical studies have demonstrated its potential to improve understanding of healthy heart function as well as how microstructural dysfunction contributes to disease. This new cardiac imaging biomarker will have important diagnostic, prognostic and therapy follow-up value. However, currently available in-vivo DT-CMR acquisition techniques are limited by low spatial resolution, poor coverage, multiple breath holds and long acquisition times, hampering its translation into clinical routine. This project will investigate simultaneous multi-slice (SMS) techniques, which acquire data from multiple 2D slices at the same time. Despite initial proof-of-principle studies demonstrating SMS DT-CMR, very few clinical SMS DT-CMR studies exist. We will investigate SMS DT-CMR and 3D DT-CMR to develop a robust clinical tool with improved coverage and efficiency. We will also investigate respiratory feedback methods to enable free-breathing acquisitions for whole heart coverage.

Project 9 - Investigation of microscopic conduction block in myocardial slices and whole hearts

SupervisorsFu Siong Ng
Assistant supervisorDavid Pitcher

Atrial fibrillation is the most common heart rhythm abnormality in the UK, with increasing prevalence in with age, and the cause of significant mobility and mortality. There is currently intense unresolved debate around them mechanisms that underlie atrial fibrillation, with conflicting clinical data in humans providing evidence. The main postulated mechanisms that underlie the persistence of atrial fibrillation include a sprial wave or a microreentrant circuit driving the fibrillation, versus random chaotic multiple wavelet reentry. There significant controversy around the rotor hypothesis, which describes a rotational activation around an area of tissue that is excitable but not excited, a form of functional block termed phase singularity. Work in our lab has shown microscopic analysis of these rotors in cell monolayers shows microscopic line of block (200-600um in length and 8-14um in width) and not functional block as previously described. The work of this PhD would be to investigate rotational activity in whole heart and 300um thick slices investigating fibrilatory dynamics at the centre of this rotational activity

Project 10 - New Clinical Insights from Data Acquired From Tech-Enabled Healthcare Pathways: Connected Care, Big Data, Machine Learning

Primary SupervisorNick Peters
Co-supervisorAnil Bharath

The Connected Care Bureau (CCB) has been established as a joint venture between Imperial College and Imperial NHS Trust to monitor and help manage patients with long-term conditions in the community. These innovative care pathways are enabled by approved healthcare technologies from which data are received by CCB to inform patient management, but also provide large volumes of related and unrelated data that can be expected to give important insights into ill health and outcomes and help define whole new perspectives and even new fields of clinical science. This will require the existing College expertise in Data Science and, in particular, Machine Learning, and has associated opportunities to work with the CCB collaborations with entities such as DigitalHealth.London, Google, other care organisations.

Project 11 - ElectroCardioMaths Virtual Heart: a multi-scale framework for multi-modal data fusion, mechanistic insight and personalised modelling

SupervisorsNick Peters
Co-supervisor: Rasheda Chowdhury, Chris CantwellAnil Bharath

Atrial Fibrillation (AF) is a disease where abnormal electrical signals lead to mal-coordination of the contraction of the heart, resulting in reduced quality of life and increased risk of stroke. Treatment through catheter ablation has low success rates with little improvement for decades. Novel treatment development requires insight incorporating traditional and novel measurements. The ElectroCardioMaths group has unique access to explanted human hearts, both diseased and control, providing unprecedented levels of biological measurement across a spectrum of modalities including electrogram recordings, optical voltage mapping, MRI/CT and histology. This project will develop the computational framework - a virtual heart - to reconstruct macroscopic and microscopic chamber geometry and fuse these data with electrophysiology, using them to gain insight into the mechanisms driving AF and to calibrate and validate computational modelling. This will further act to build confidence in clinical modelling as a tool to safely inform diagnosis and design treatment for patients.

Project 12 - Tolerogenic nanoparticles for dendritic cell immunotherapy

SupervisorsSusanne Sattler
Co-supervisors: Nadia RosenthalMolly Stevens

Breakdown of immunological tolerance causes autoimmunity, allergies and a range of hyper-inflammatory conditions, including myocarditis and post-infarct auto-immunity leading to heart failure. Tolerogenic dendritic cells (DC) have been shown to be able to re-establish tolerance and have been successfully used in phase I clinical trials for immunotherapy in type 1 diabetes and rheumatoid arthritis. However, current DC based strategies rely on isolation, ex vivo processing and re-infusion of dendritic cells. This project aims to engineer DC-targeting nanoparticles for direct in vivo application to induce tolerance against selected cardiac proteins to suppress auto-reactivity against the heart. Particles will be optimised to (1) specifically target DC, (2) efficiently deliver antigen load in vitro and in vivo, (3) maintain an immature/tolerogenic phenotype of DC, and (4) enable DC to induce antigen specific regulatory T cells.

Project 13 - Development of biocompatible microparticle drug delivery platforms to treat drug resistant pulmonary tuberculosis (TB)

SupervisorsTerry Tetley
Co-supervisor: Ajit LalvaniAlexandra Porter

Mycobacterium tuberculosis (M-tb) causes TB; M-tb can resist antibiotic treatment inducing antimicrobial resistance (AMR). Antibiotic potency depends on reduced functional integrity of the bacterial wall, unaffected in AMR TB. We aim to use silver (Ag) and zinc oxide (ZnO) nanoparticles, antimicrobials that cause increased bacterial permeability, to enable effective drug delivery. We are developing biocompatible microparticulate drug delivery platforms (Ag and ZnO nanoparticles and drug combinations) to by-pass M-tb cell wall barrier and deliver small molecule antituberculous drugs, with increased potency, to the extracellular and intracellular compartment of M-tb and AMR M-tb in infected pulmonary alveolar macrophages. A multidisciplinary project between NHLI (Medicine) and Materials Science (Engineering), involving microparticle drug construction, unique in vitro human co-culture exposure models of the alveolar unit and high resolution 3D imaging techniques to measure efficacy of novel drug delivery platforms.

Project 14 - Biophysical and computational tools for rational design of new generation genetically encoded voltage indicators for cardiac optogenetics

Primary SupervisorsLiming Ying
Co-supervisor: Thomas KnopfelMauricio Barahona

Monitoring and controlling cardiomyocyte activity with optogenetic tools offer exciting opportunities for cardiovascular research. Genetically encoded voltage indicators (GEVIs) are particularly attractive for minimal invasive and repeated assessments of cardiac excitation from the cellular to the whole heart level. GEVIs based on voltage-sensing domain (VSD) of ion channels can be adapted to optical mapping of the heart. We aim to develop a biophysical tool to monitor the dynamic motion of the VSD in action using single molecule fluorescence. The experiments will be enhanced with graph-theoretical analyses of the protein structures using two computational methods, Markov Stability and Propensity, which will serve to establish key structural features and mutations. The new methodology will allow us to link the relationship between fluorescence read-out and VSD conformation, and to determine the key residues governing the transient fluorescence response of the VSD. This knowledge will then be used for the development of new GEVIs.

Project 15 - Machine learning approaches to dissect mechanisms of tumour progression

Co-supervisorsVania Braga and Ben Glocker

The project will investigate the fundamental mechanisms of how cancerous cells detach from solid tumours and disseminate into other body sites. The aim is to identify potential targets for therapeutic approaches to block or delay metastatic processes in cancer patients. Using machine learning and computer vision approaches, the student will train the computer to identify patterns of disruption of cell adhesion caused by distinct oncogenes and other stimuli. The association of patterns of disruption with molecular profiles will help to determine causal relationships and therapeutic approaches. The student will analyse a large image dataset in a high-throughput manner and validate the robustness, reproducibility and automation of the machine learning process. This study will address an unmet need in functional and translational screens by enabling the identification of parameters of biological relevance for cancer metastasis.

How to apply

  • Applications are invited from candidates with a Master’s degree or equivalent in appropriate discipline. Applicants must hold, or expect to obtain, a first or upper second-class undergraduate degree or UK equivalent, in an appropriate subject from a recognised academic institution, and must meet College entry requirements.
  • You must select your preferred project from the list of available projects. Please include your preferred project choice in your application. Your application must include a two-page CV and 500-word statement detailing why you would like to apply for this scheme.
  • The application will be shortlisted and, if successful, you will be invited to attend an interview by the relevant department and submit a formal application to the College.

Please submit your project choice and application documents to Eleanor Tucker ( by 10 June 2018.

Residence eligibility guidance

Please refer to the EPSRC student eligibility webpages for full details, and see below for the residence eligibility criteria.

To be eligible for a full award a student must fulfil all the following criteria:

  • Have settled status in the UK, meaning they have no restrictions on how long they can stay.
  • Been ‘ordinarily resident’ in the UK for three years prior to the start of the studentship. This means they must have been normally residing in the UK (apart from temporary or occasional absences).
  • Have not been residing in the UK wholly or mainly for the purpose of full-time education (this does not apply to UK or EU nationals).