Perfusion imaging allows us to measure the vital role played by delivery of blood to the brain in keeping it supplied with nutrients and removal of waste. Any deviations of the blood supply from normal can be a sign of disease. In particular early and subtle changes in perfusion might mark regions of the brain which are affected by degenerative diseases such as dementia before other imaging signs become obvious. The technology exists and is increasingly widely available to image perfusion quickly and safely using Magnetic Resonance Imaging. Thus perfusion Magnetic Resonance Imaging could be a valuable tool in the understanding of dementias, as well as the diagnosis and monitoring of patients with dementia. The challenge that remains is making sufficiently specific measurements of subtle changes in blood supply that would be needed to make the technology truly useful for patients. This project addresses that problem in three ways: > Automated removal of errors associated with imperfect measurement, for example due to motion of the patient. > Methods to control for differences between patients due to their individual brain structure, allowing blood supply measurements to be compared between individuals or from a patient to a population of similar healthy adults. These methods remove uncertainties introduced by other differences between the brain's of individuals that are not related to perfusion. > Generation of personalised reference perfusion images for an individual patient against which their measured perfusion can be compared to detect changes specific to that individual. The methods and tools that are to be generated in this project will enable perfusion Magnetic Resonance Imaging to be used more effectively in the UK-wide effort to understand dementia and in the search for new and effective treatments. Ultimately the work done in this project will enable perfusion Magnetic Resonance Imaging to become a valuable clinical tool that can be used in the diagnosis and monitoring of individual patients with dementia.

Perfusion imaging allows us to measure the vital role played by delivery of blood to the brain in keeping it supplied with nutrients and removal of waste. Any deviations of the blood supply from normal can be a sign of disease. In particular early and subtle changes in perfusion might mark regions of the brain which are affected by degenerative diseases such as dementia before other imaging signs become obvious. The technology exists and is increasingly widely available to image perfusion quickly and safely using Magnetic Resonance Imaging. Thus perfusion Magnetic Resonance Imaging could be a valuable tool in the understanding of dementias, as well as the diagnosis and monitoring of patients with dementia. The challenge that remains is making sufficiently specific measurements of subtle changes in blood supply that would be needed to make the technology truly useful for patients. This project addresses that problem in three ways: > Automated removal of errors associated with imperfect measurement, for example due to motion of the patient. > Methods to control for differences between patients due to their individual brain structure, allowing blood supply measurements to be compared between individuals or from a patient to a population of similar healthy adults. These methods remove uncertainties introduced by other differences between the brain's of individuals that are not related to perfusion. > Generation of personalised reference perfusion images for an individual patient against which their measured perfusion can be compared to detect changes specific to that individual. The methods and tools that are to be generated in this project will enable perfusion Magnetic Resonance Imaging to be used more effectively in the UK-wide effort to understand dementia and in the search for new and effective treatments. Ultimately the work done in this project will enable perfusion Magnetic Resonance Imaging to become a valuable clinical tool that can be used in the diagnosis and monitoring of individual patients with dementia.

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.