MRI scanners are used widely to diagnose disease and to understand the workings of the healthy body. However, while useful for some diagnoses, they do not capture tissue properties at microscopic length scales (thousandths of a millimetre) where important processes occur, e.g. in the 'axons' connecting different brain areas, or in cells in vital organs, e.g. liver. Such detailed examination usually requires an invasive 'biopsy' which is studied under a microscope. However, biopsies only provide information about small regions of an organ, are destructive and so cannot be performed repeatedly for monitoring, and can be risky to collect, e.g. in the brain. This project assembles engineers, physicists, mathematicians and computer scientists to develop new MRI methods for quantifying tissue structure at the microscopic scale. The principal approach looks at how fine tissue structure impedes the movement of water. Current MRI hardware restricts measurement to relatively large molecular displacements and from tissue components with a relatively strong and long-lived signal. This blurs our picture and prohibits us from quantifying important characteristics, such as individual cell dimensions, or packing of nerve fibres. The sensitivity of MRI to smaller molecular movements and weaker signals is mainly limited by the available magnetic field gradients (controlled alterations in the field strength within the scanner). We have persuaded MRI manufacturers to build a bespoke MRI system with ultra-strong gradients (7 times stronger than available on standard MRI scanners) to be situated in the new Cardiff University Brain Research Imaging Centre. One similar system currently exists (in Boston, USA) but is used predominantly to make qualitative pictures of the brain's wiring pattern. Our team has the unique combination of expertise to develop and exploit this hardware in completely new directions. By designing new physics methods to 'tune' the scanner to important (otherwise invisible) signals, developing new biophysical models to explain these signals, and suppressing unwanted signals, we will be able to quantify important tissue properties for the first time. Making such a system usable poses several key engineering challenges, such as modelling of electromagnetic fields, to deal with confounds that become significant with stronger gradients, and modelling of the effects on nerves/cardiac tissue, to impose safety constraints. However, the current work of the consortium of applicants provides strong starting points for overcoming these challenges. Established methods for accelerating MR data acquisition will be compromised with stronger gradients, requiring development of new physics methods for fast data collection. Once achieved, faster acquisition and access to newly-visible signal components will enable us to develop new mathematical models of microstructure incorporating finer length-scales to increase understanding of tissue structure in health and disease, and to make testable predictions on important biophysical parameters such as nerve conduction velocities in the brain. This will result in earlier and more accurate diagnoses, more specific and better-targeted therapy, improved treatment monitoring, and overall improved patient outcome. The ultimate goal is to develop the imaging software that brings this hardware to mass availability, in turn enabling a new generation of mainstream microstructure imaging and macrostructural connectivity mapping techniques to translate to frontline practice.

The increasing occurrence of dementia within our ageing population is one of the pressing challenges facing society. Successful management of patients with dementia is significantly aided by early and accurate diagnosis. Imaging methods such as magnetic resonance imaging (MRI) and positron emission tomography (PET) are already used in the diagnostic process; we believe that there is substantial scope for both methods to be improved to provide more precise and sensitive diagnostic information, and to do so in a way that is easily tolerated by patients. If we are correct in this belief, then the methods we develop within this project will not only help in early diagnosis, but may also help in the discovery of new therapies and in the longer term with helping doctors select the best therapeutic strategies for patients with different forms of dementia. Imaging methods such as MRI and PET can tell us a lot more about brains than simply providing a picture of brain shape and size. We will focus on improving MRI and PET to be sensitive to two important microscopic aspects of dementia. Firstly, we will develop and validate new methods for measuring the loss of brain cells due to the condition; this loss is the cause of many of the symptoms of dementia, such as memory problems, and we hope to be able to detect these changes earlier than has previously been possible. Secondly we will develop and validate new methods for measuring changes in blood delivery to the brain and how this can affect oxygen delivery. These changes are thought to be part of one of the important processes involved in causing cell death and tissue loss, and are likely to be particularly relevant to vascular dementia. We will also spend considerable time checking that the measurements we develop are both accurate and practical for application in dementia patients in the future. We will optimise the way in which the scanning processes take place so that the time required for patients to lie in the scanner(s) is minimised. This will be important for future adoption of these methods in the clinical environment.

Lung diseases such as Asthma and Chronic Obstructive Pulmonary Disease affect one in five people in the UK and kill someone every 5 minutes. The number of patients with these lung diseases was increasing in the NHS even before COVID-19. We are also learning about serious long-term effects of COVID-19 that will add to the existing burden on the NHS. There have been huge advances in technologies that allow scientists to see inside the lungs and measure what we breathe out. While this information has taught us quite a lot, it is still very difficult to combine different sources of information and turn it into new or improved treatments. Getting that useful information out of large amounts of medical test results requires sophisticated physics-based mathematical and statistical models run on powerful computers - a combination of techniques called data-driven biophysical multiscale modelling. The ability to develop those kinds of models will allow us to better understand how diseases start and how they progress. Our BIOREME network will support new research that uses these techniques to mimic biological and mechanical processes that occur throughout the lung. Using the information from thousands of lung tests, the idea is then to get these models to mimic real diseased lungs. In order to improve and build trust in these models, some of our projects will be focused on comparing their outputs to results from other lung tests. Medical scientists can then use such models to test what might happen in a particular type of lung disease, and to investigate possible responses to new treatments before testing these in patients. Most importantly, this will lead to the design of new drugs and improved trials for new treatments. The first step will be to get medics, imaging experts and mathematicians together with industry and patient group representatives to decide on which specific research areas to prioritise, where this form of modelling will make the most difference. This NetworkPlus award will then allow us to organise multiple events, in different formats, designed to help researchers to collaborate, and to come up with the best initial projects to help achieve our goals. We will then help the researchers to develop these into larger projects that will attract funding from other sources and continue the research into the future. Even after this funding runs out, BIOREME will provide a lively forum for lung researchers to continue solving problems using these advanced computational tools. Finally, BIOREME will support outreach activities to engage and educate communities and young people in the role that mathematics can play in medicine and healthcare, and to inspire a new generation of respiratory scientists from diverse backgrounds.