To increase our understanding of how the brain works and how it goes wrong in people with neurological conditions such as epilepsy we need to develop better systems for making measurements of brain activity. Electroencephalography (EEG) is an important modality in clinical and experimental neuroscience capable of measuring electrical changes occurring with sub-millisecond temporal resolution and a spatial resolution of centimetres while functional Magnetic Resonance Imaging (fMRI) can map haemodynamic changes over the entire the brain at a time-scale of seconds and spatial resolution of a few millimetres or better. Therefore together they can perform measurements across a greater range of brain activity occurring either at faster temporal or smaller spatial scales. However, during simultaneous EEG-fMRI acquisitions these both methods signal are degraded by noise related to motion, thereby significantly limiting the sensitivity of this type of study so far. The purpose of this application is to build a robust system that can perform these simultaneous EEG and fMRI measurements of brain activity. To achieve this goal we will integrate new fast fMRI pulse sequences because images obtained in shorter time intervals are intrinsically less motion sensitive (like a faster shutter speed on a camera). Also better modelling of motion will be possible if we obtain more images per unit time because we can better separate and model the different sources of signal and noise that occur in different frequency ranges. In addition, we will optimise motion detection and prospective motion correction (PMC) using a camera system that tracks the subject's motion and updates the image acquisition process so that patient motion is supressed. When using these improvements to fMRI data acquisition we will need to develop novel EEG artefact correction methods for simultaneous in-scanner EEG recording which currently rely on the repetitive nature of the artefact in time. Both motion and PMC are likely to make the artefact more variable and so will require the development of novel correction methods. Once we have developed this system we will apply it to ten patients with hard to treat epilepsy from Great Ormond Street Hospital who are being assessed for epilepsy surgery. This assessment aims to identify the epileptic brain regions and EEG-fMRI is a tool to help obtain this information. We will compare our current standard EEG-fMRI protocol which often suffers from degradation due to motion (particularly in young children) to our new robust EEG-fMRI incorporating PMC, fast fMRI and improved EEG artefact correction.

We propose to create the EPSRC Centre for Doctoral Training (CDT) in intelligent integrated imaging in healthcare (i4health) at University College London (UCL). Our aim is to nurture the UK's future leaders in next-generation medical imaging research, development and enterprise, equipping them to produce future disruptive healthcare innovations either focused on or including imaging. Building on the success of our current CDT in Medical Imaging, the new CDT will focus on an exciting new vision: to unlock the full potential of medical imaging by harnessing new associated transformative technologies enabling us to consider medical imaging as a component within integrated healthcare systems. We retain a focus on medical imaging technology - from basic imaging technologies (devices and hardware, imaging physics, acquisition and reconstruction), through image computing (image analysis and computational modeling), to integrated image-based systems (diagnostic and interventional systems) - topics we have developed world-leading capability and expertise on over the last decade. Beyond this, the new initiative in i4health is to capitalise on UCL's unique combination of strengths in four complementary areas: 1) machine learning and AI; 2) data science and health informatics; 3) robotics and sensing; 4) human-computer interaction (HCI). Furthermore, we frame this research training and development in a range of clinical areas including areas in which UCL is internationally leading, as well as areas where we have up-and-coming capability that the i4health CDT can help bring to fruition: cancer imaging, cardiovascular imaging, imaging infection and inflammation, neuroimaging, ophthalmology imaging, pediatric and perinatal imaging. This unique combination of engineering and clinical skills and context will provide trainees with the essential capabilities for realizing future image-based technologies. That will rely on joint modelling of imaging and non-imaging data to integrate diverse sources of information, understanding of hardware the produces or uses images, consideration of user interaction with image-based information, and a deep understanding of clinical and biomedical aims and requirements, as well as an ability to consider research and development from the perspective of responsible innovation. Building on our proven track record, we will attract the very best aspiring young minds, equipping them with essential training in imaging and computational sciences as well as clinical context and entrepreneurship. We will provide a world-class research environment and mentorship producing a critical mass of future scientists and engineers poised to develop and translate cutting-edge engineering solutions to the most pressing healthcare challenges.

Medical imaging has transformed clinical medicine in the last 40 years. Diagnostic imaging provides the means to probe the structure and function of the human body without having to cut open the body to see disease or injury. Imaging is sensitive to changes associated with the early stages of cancer allowing detection of disease at a sufficient early stage to have a major impact on long-term survival. Combining imaging with therapy delivery and surgery enables 3D imaging to be used for guidance, i.e. minimising harm to surrounding tissue and increasing the likelihood of a successful outcome. The UK has consistently been at the forefront of many of these developments. Despite these advances we still do not know the most basic mechanisms and aetiology of many of the most disabling and dangerous diseases. Cancer survival remains stubbornly low for many of the most common cancers such as lung, head and neck, liver, pancreas. Some of the most distressing neurological disorders such as the dementias, multiple sclerosis, epilepsy and some of the more common brain cancers, still have woefully poor long term cure rates. Imaging is the primary means of diagnosis and for studying disease progression and response to treatment. To fully achieve its potential imaging needs to be coupled with computational modelling of biological function and its relationship to tissue structure at multiple scales. The advent of powerful computing has opened up exciting opportunities to better understand disease initiation and progression and to guide and assess the effectiveness of therapies. Meanwhile novel imaging methods, such as photoacoustics, and combinations of technologies such as simultaneous PET and MRI, have created entirely new ways of looking at healthy function and disturbances to normal function associated with early and late disease progression. It is becoming increasingly clear that a multi-parameter, multi-scale and multi-sensor approach combining advanced sensor design with advanced computational methods in image formation and biological systems modelling is the way forward. The EPSRC Centre for Doctoral Training in Medical Imaging will provide comprehensive and integrative doctoral training in imaging sciences and methods. The programme has a strong focus on new image acquisition technologies, novel data analysis methods and integration with computational modelling. This will be a 4-year PhD programme designed to prepare students for successful careers in academia, industry and the healthcare sector. It comprises an MRes year in which the student will gain core competencies in this rapidly developing field, plus the skills to innovate both with imaging devices and with computational methods. During the PhD (years 2 to 4) the student will undertake an in-depth study of an aspect of medical imaging and its application to healthcare and will seek innovative solutions to challenging problems. Most projects will be strongly multi-disciplinary with a principle supervisor being a computer scientist, physicist, mathematician or engineer, a second supervisor from a clinical or life science background, and an industrial supervisor when required. Each project will lie in the EPSRC's remit. The Centre will comprise 72 students at its peak after 4 years and will be obtaining dedicated space and facilities. The participating departments are strongly supportive of this initiative and will encourage new academic appointees to actively participate in its delivery. The Centre will fill a significant skills gap that has been identified and our graduates will have a major impact in academic research in his area, industrial developments including attracting inward investment and driving forward start-ups, and in advocacy of this important and expanding area of medical engineering.