Electrical Impedance Tomography (EIT) is a novel medical imaging method in which tomographic "slice" images are rapidly produced using rings of electrodes placed around the body. It is safe, portable and inexpensive. The principal applicant's group has demonstrated that EIT can rapidly image functional brain activity in neurological conditions like stroke, epilepsy and normal activity in animal models and has developed systems which work well in head-shaped tanks. Thrombolytic "clot-busting" treatment is a new treatment for acute stroke which must be given within three hours, but its take-up has been restricted because it is essential to undertake imaging of the head before it can be administered. This is because a sudden onset of weakness or disability which appears as a stroke, can be due to insufficient blood to a part of the brain or else bleeding into the brain. The clot-dissolving agent cannot be given until imaging has been used to assess if a bleed has occurred, as it could make the bleeding much worse with catastrophic consequences. EIT has the potential to provide an inexpensive portable unit for use in ambulances or GP surgeries which would revolutionise administration of this drug in acute stroke by providing imaging at the point of contact. This could be relayed over the internet to a radiologist who could then give permission for a paramedic to give the drug in the ambulance or in a remote centre. The plan is to make three technical improvements in imaging and design of a helmet or headnet containing the contacts needed for accurate brain EIT and test these objectively in tanks, anaesthetised rats and human patient studies. The final outcome will be a new EIT system design optimised for imaging in acute stroke, with rigorous evaluation of its performance in about 30 patients.

Vaccines are the most successful public health initiative of the 20th century. They save millions of lives annually, add billions to the global economy and extended life expectancy by an average of 30 years. Even so, the UN estimates that globally 6 million children each year die before their 5th birthday. While vaccines do exist to prevent these deaths, it is limitations in manufacturing capacity, technology, costs and logistics that prevent us for reaching the most vulnerable. The UK is a world leader in vaccine research and has played a significant leadership role in several public health emergencies, most notably the Swine Flu pandemic in 2009 and the recent Ebola outbreak in West Africa. While major investment has been made into early vaccine discovery - this has not been matched in the manufacturing sciences or capacity. Consequently, leading UK scientists are forced to turn overseas to commercialise their products. Therefore, this investment into The Future Vaccine Manufacturing Hub will enable our vision to make the UK the global centre for vaccine discovery, development and manufacture. We will create a vaccine manufacturing hub that brings together a world-class multidisciplinary team with decades of cumulative experience in all aspects of vaccine design and manufacturing research. This Hub will bring academia, industry and policy makers together to propose radical change in vaccine development and manufacturing technologies, such that the outputs are suitable for Low and Middle Income Countries. The vaccine manufacturing challenges faced by the industry are to (i) decrease time to market, (ii) guarantee long lasting supply - especially of older, legacy vaccine, (iii) reduce the risk of failure in moving between different vaccine types, scales of manufacture and locations, (iv) mitigating costs and (v) responding to threats and future epidemics or pandemics. This work is further complicated as there is no generic vaccine type or manufacturing approach suitable for all diseases and scenarios. Therefore this manufacturing Hub will research generic tools and technologies that are widely applicable to a range of existing and future vaccines. The work will focus on two main research themes (A) Tools and Technologies to de-risk scale-up and enable rapid response, and (B) Economic and Operational Tools for uninterrupted, low cost supply of vaccines. The first research theme seeks to create devices that can predict if a vaccine can be scaled-up for commercial manufacture before committing resources for development. It will include funds to study highly efficient purification systems, to drive costs down and use genetic tools to increase vaccine titres. Work in novel thermo-stable formulations will minimise vaccine wastage and ensure that vaccines survive the distribution chain. The second research theme will aim to demystify the economics of vaccine development and distribution and allow the identification of critical cost bottlenecks to drive research priorities. It will also assess the impact of the advances made in the first research theme to ensure that the final cost of the vaccine is suitable for the developing world. The Hub will be a boon for the UK, as this research into generic tools and technologies will be applicable for medical products intended for the UK and ensure that prices remain accessible for the NHS. It will establish the UK as the international centre for end-to-end vaccine research and manufacture. Additionally, vaccines should be considered a national security priority, as diseases do not respect international boundaries, thus this work into capacity building and rapid response is a significant advantage. The impact of this Hub will be felt internationally, as the UK reaffirms its leadership in Global Health and works to ensure that the outputs of this Hub reach the most vulnerable, especially children.

Biomedical imaging has a crucial role in (pre)clinical research, drug development, medical diagnosis and assessment of therapy response. Often, the images are tomographic: from the measured data, (stacks of) slices or volumes representing anatomical and functional properties of the patient can be reconstructed using sophisticated algorithms. Increasingly, images from multiple types of systems such as Magnetic Resonance (MR), radionuclide imaging using Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) and X-ray Computed Tomography (CT) are analysed together. Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data require considerable expertise and effort in software implementation. In our previous CCP on synergistic reconstruction for PET-MR, we created a network of UK and international researchers working towards integrating image reconstruction of data from integrated, simultaneous, PET-MR scanners. New multi-modality systems are now available or under development, for instance SPECT-MR or even tri-modality PET-SPECT-CT systems. At the same time, top-of-the-range multi-modality systems are expensive and instead combining single-modality scans from different time-points and systems can provide more cost-effective solutions in some cases. Synergistic image reconstruction aims to exploit the commonalities between the data from the different modalities and time points. However, cross-modality methods are particularly challenging. We will therefore extend the network to exploit synergy in multi-modal, multi-contrast, multi-time point information for biomedical applications, concentrating on the logistical and computational aspects of synergistic image reconstruction. The Open Source Software platform to be provided by this CCP will be an enabling technology which removes the frequent obstacles encountered when working with the raw medical imaging datasets, accelerating innovative developments in image reconstruction, and ultimately enabling the possibility of synergistic image reconstruction by establishing validated pipelines for processing raw data of multiple data-sets.

Our vision is to revolutionise diagnosis, risk stratification and therapy for people with cancer based on innovations in magnetic resonance (MR) technology. The new system of care will be based on developing a strategy of optimized 'target generation', specific 'target verification' and precise 'target destruction' We use imaging to find out where cancer lies within the body. We call the process of taking an image to find a lesion 'target generation'. Whilst imaging helps to find disease we don't always know whether a lesion on an image is cancer or whether it is benign. Also, if a lesion is cancer we don't often know what that means for the individual patient. By way of explanation, we may know that a particular distribution of cancer spread within the body confers a poorer outcome for a group of patients e.g. a 50% of patients survive after 5-years; however, we often can't tell whether an individual patient will fall into the 50% that survive. Understanding the nature of a lesion detected on imaging and how this impacts on treatment choices and overall treatment outcome is termed 'target verification'. Our proposal seeks to establish a platform to allow new methods and advances we have been making in pre-clinical 'target generation' to be robustly and rapidly developed for clinical use. It also seeks to develop the best imaging technologies and combine these with an understanding of the cellular and molecular environment of cancers in order to significantly improve 'target verification'. The third aspect to our proposal is the development of new technologies to deliver precise treatment to individual cancer sites. We term this 'target destruction'. A range of technologies exist and we aim to provide the environment where the best and most promising of these can be easily translated from conception of idea all the way to clinical application. We have a strong track record in this area with therapies for prostate cancer and we seek to extend this to other cancer sites, initially lung and gastrointestinal cancers. Our proposal brings together a group of scientist with complementary skills in imaging, surgery and engineering in one location through establishing a dedicated Centre for Image Guided Therapy within University College London. Within this we seek to establish a translational imaging facility, which will reflect the type of imaging available within the clinical environment, and will also allow scientists access to the cutting-edge equipment to enable them to develop technological advances rapidly and robustly to a stage that they can be applied for patient benefit.

Imagine a world in which every individual can be routinely and extensively health monitored, in a time-efficient and safe manner, without having to visit an oversubscribed, centralised medical centre with limited access and appointment flexibility. Imagine a new clinical paradigm where early diagnosis becomes the standard, even in remote areas, within low-income demographics and for international travel, due to ubiquitous, modular, high-resolution X-ray imaging systems with automated diagnosis and live reporting; where frequent imaging contributes to a large diagnostic portfolio of individuals over time (whilst maintaining privacy) and advanced artificial-intelligence (AI)-based algorithms use these anonymous data sets acquired across the population to identify extremely early stages of disease - transforming preventative medicine as we know it. This is the 2050 that ReImagine will enable. We will revolutionise the use of X-rays for medical imaging through low-dose, high-resolution and inexpensive computed tomography (CT) scanners, where highly innovative hardware and software components will be developed side-by-side to enable automated all-in-one pre-symptomatic diagnosis. Our vision will be enabled by developing highly sensitive X-ray detectors using scalable halide perovskite (PVK) semiconductors - materials currently making impact as disruptive photovoltaic (PV) technologies - for phase contrast X-ray imaging, in conjunction with AI-driven algorithms for image reconstruction, lesion detection and segmentation. This will realise quicker and more efficient healthcare delivery and prevent disease spread through extremely early detection of disease (e.g., those otherwise responsible for future pandemics) and for routine follow-up of oncology patients (e.g. early detection of cancer recurrence). To realise this extremely challenging vision - combining breakthroughs in hardware, software and end-user application - we have uniquely assembled a world-leading, cross-cutting team from the Universities of Cambridge, Loughborough and Leicester, together with academic partners at the University of Leiden and industry partners in GE Healthcare, Scintacor, Cheyney and Immaterials Labs, bringing combined expertise spanning materials synthesis and scaling, characterisation and modelling, device assembly, detector physics, mathematics, CT systems development, and clinical radiology. The hardware will be interweaved with the software and algorithm development, with both guided by clinical insight, industry and case studies to ensure fit for end users.