Personalising care, i.e. tailoring therapeutic recommendations to people's individual health needs, has always been a clinicians' goal throughout the history of medicine. But never before has it been possible to design interventions and to predict how our bodies will respond to those. New possibilities are now emerging as we bring together novel approaches, such as state-of-the-art imaging and modelling and simulation. The NHS Long Term Plan identifies cardiovascular disease as a clinical priority and the single biggest condition where lives can be saved by the NHS over the next 10 years. There are currently over 43000 often life-saving vascular interventions p/year in England alone, predicted to increase due to an ageing population and rise in co-morbidities. Many of these interventions require surgery and/or permanent and personalised vascular implants. Vascular surgeons rely on superb skill and flair to perform some of the most complex (and life-critical) interventions; patients, on the other hand, rely on these interventions being safe or high-performing, for a lifetime. But how do we know that this will be the case? That these interventions are optimal? Getting the right intervention (often, surgical) to the right patient, at the right time, i.e. precision vascular surgery, has until now, been an unachievable goal. To realise this goal, we require transformative engineering technologies, fundamentally different from those used today. For the vascular surgery of the future to become a reality, we need pioneering work able to predict the future outcome of an individualised vascular intervention with an acceptable level of realism, fast enough to allow the exploration of multiple possibilities in short periods of time, and trustworthy enough such that they elicit trust and confidence from clinical practitioners. Blood flow (haemodynamics) plays a pivotal role in the initiation and progression of most vascular conditions and the clinical outcomes of interventions. However, hemodynamic information is not readily available in routine clinical practice -despite advances in medical imaging- where a variety of imaging modalities are used routinely. More crucially, imaging data can only give us information about the present, not the future; they cannot tell us what the outcome of any given -often personalised- intervention will be. Here is a case where engineering tools can make a real difference by providing blood flow information for vascular diseases, that cannot be measured in vivo and more importantly, by creating computer models of potential interventions, and their outcomes. By fusing computational blood flow models and imaging data we can make a real breakthrough in clinical pre-operative planning and personalise treatment. In PIONEER we plan to develop the most sophisticated, physics-driven computational tools that will extract, in real-time, accurate unsteady and three-dimensional hemodynamic information (velocity and pressure) from routinely used vascular imaging data. This information will be used for haemodynamic virtual prototyping of personalised cardiovascular interventions and tailoring of cardiovascular devices. The work will enable a fundamental step forward towards precision vascular surgery and will provide expert support for vascular surgeons in their decision-making process, leading to a dramatic improvement in the management of individual patients' risk. To catalyse this vision, we will work synergistically with three top hospitals in the country (Royal Free Hospital, Barts Hospital and GOSH), two patient groups (AVM Butterfly Charity and Aortic Awareness UK) and a leading medical device company, Terumo Aortic. Together, we will firstly create a proof of concept that will pave the way to introduce our ground-breaking technology in clinical and manufacturing workflows.

KEYWORDS: chemistry, catalysis, image processing, manufacturing, productivity, EPSRC. The digital eyes of cameras paint the colourful worlds we can and cannot see by numbers. These numbers have the power to help us make life-changing medicines on timescales that, at the present time, we cannot imagine. This research and leadership programme focusses on developing the analytical power of digital cameras to improve the productivity and safety of chemical manufacturing. The Pharmaceutical sector is the UK's second largest in terms of income, but it is an extremely costly business to run. This cost burden hits at the heart of one of the UK's biggest challenges: our lack of productive output versus hours worked compared with other nations. To this point, 'Big Pharma' stands to realise a >£1bn reduction in R&D cost by 2030, but only if the efficiency with which it can discover new medicines can be improved by a third beyond the current state of the art. How can we make new medicines more productively? Fast adoption of digital technologies is vital. As linked to the core of the proposed research, digital technology adoption should include the amazing ability of cameras to tell the story of the world, not in words, but in useful numbers. In Big Pharma, to understand whether or not the chemical process of making a medicine is safe to use on the manufacturing scale, we need to be able to analyse the chemical process in real time. The better we analyse a process on the small scale, the better its chances of being used productively to make medicines on the large scale. However, many useful reactions are never applied in industry because they do not meet the strict criteria for safe application on the manufacturing scale. This is an unsolved problem, and no current chemical monitoring technologies can seamlessly analyse chemical processes on small lab scale, large plant scale, and in dangerous environments. If such a monitoring technology were available, it has the potential to lead to an up-to 9:1 return on investment, moving us closer to the ultimate goal of improving research productivity by a full third. Computer Vision is the science of digitally quantifying real-world objects using cameras. It is a vibrant area of research with a rich history in astronomy, land surveys, autonomous systems, food safety, defence and security, and art forensics, among other areas. Whilst 'photo-style' camera analysis has been used over the past decade, new and unique methods of using real-time camera-based chemical monitoring is still hugely underdeveloped across chemical manufacturing, despite the wealth of emerging knowledge from seemingly unrelated scientific disciplines. The untapped technology of camera-enabled reaction monitoring thus holds remarkable fundamental research potential. A new research programme in this area would contribute strongly to UK chemical manufacturing, realising significant and digitally-adoptive increases in productivity 2-3 years ahead of current 2030 targets. This ambitious research programme will deliver a world-leading suite of new camera-enabled analytics for understanding a wide range of valuable chemical processes to make them safer and more productive on scale. The research leader has an emerging track record which has already directed step-changes in homogeneous catalyst design, reaction kinetics platforms, safety software systems, and industrial technology translation. Bordering chemistry and computer Science, this programme will deliver research excellence in video analysis methods for visible and invisible chemical processes, across all scales of chemical development, and in a wide range of chemistries beyond the core focus of improving productivity in Pharmaceutical development.