Imaging is a multidisciplinary research area. Positron emission tomography (PET), a non-invasive molecular imaging technique, relies on the availability of radiolabeled probes for molecular-level diagnostics [early diseases state detection], biological research [fundamental understanding of complex biological processes] and drug discovery [control of diseases]. PET can revolutionise these fields but its impact is limited by the worldwide shortage of skills needed to design and produce the radiotracers. This project supervised by Professor V. Gouverneur (University of Oxford - Chemistry Department), Dr Ian Newington and Dr Rajiv Bhalla (GE Healthcare Medical Diagnostics) will provide the opportunity to acquire these skills. Rapid progress in PET imaging is restricted by the cost, speed, and efficiency of radiosynthetic methods to access radiolabeled probes. 18F is identified as one of the radionuclides of choice due to its half-life of 110 mins, which allows for multistep radiosyntheses and commercial distribution. Currently, radiochemists select the site of 18F-radiolabeling to fit existing radiosynthetic methods, an approach that could be detrimental for rapid progress in PET probe discovery. 18F-Radio-retrosynthetic routes used to date are based on linear sequences of transformations designed with the aim of introducing the 18F-label ideally in the last step or as late as possible. This becomes increasingly difficult to implement when the probe is structurally complex or highly functionalised. Academics, industrialists and clinicians interested in PET-based imaging are in need of a wider range of structurally diverse radiotracers, which are currently not accessible using existing 18F-radiolabelling methods. The central proposition advanced here is that 18F-radiochemistry will benefit from convergent synthetic tactics assembling in one step a 18F-labelled component with two or more reactants simultaneously. Using this new radiochemistry, high value radiotracers will be prepared rapidly for applications in diagnostics or drug development. To validate this new conceptual framework, we selected multicomponent reactions (MCR) because these highly convergent atom- and step-economic procedures can deliver structurally diverse and complex molecules in a single step by reacting more than two substrates simultaneously. This class of reactions is attractive because the 18F-labeled precursor become integrated in the intrinsic structure of the probe and offers the possibility to introduce the 18F-label on different positions of the target probe without the need to redesign the overall radiosynthetic route. This chemistry will be validated with the preparation of targets not accessible by direct nucleophilic fluorination, for example electron rich 18F-aromatic motif. After proof of concept examining the value of 18F-labelled components for so-called 'radio-MCRs', further studies will aim at expanding the range of radio-MCRs suitable for 18F-labeling as well as the pool of radioactive building blocks and at mapping the various possible combinations of non-labeled and labeled components. Since the development of novel MCRs inclusive of asymmetric transformations is such an active area of research, a tantalizing range of opportunities emerges for the synthesis of structurally complex 18F-labeled PET tracers but also to access biomarkers for other imaging modalities. This work will establish that the inclusion of more convergent retro-radiosynthetic approach in the context of 18F-indirect labelling can greatly expand the range of 18F-labeled radiotracers made accessible for PET. This proposal represents a conceptual advance that would enlarge dramatically the scope of 18F-prosthetic group radiochemistry and provide immense benefit to diagnostics and drugs development.

We have developed unique, highly-specific tests for the detection of chemical warfare agents(CWAs) and toxic industrial chemicals (TICs). Their strengths are they: (1) display no false positives; (2) are highly sensitive; (3) are effective for volatile/ non-volatile CWAs; (4) are inexpensive; (5) require no power source; (6) work non-destructively on sensitive substrates (e.g. skin, clothing, electronics); and (7) require minimal training. We will collaborate with GE Healthcare to develop a simple, integrated swab and test-paper kits to provide a definitive, cost-effective means for the rapid, on-site detection of CWAs. The system will be suitable for civilian and military end-users.

Positron Emission Tomography (PET) is a pillar of modern diagnostic imaging, allowing non-invasive, sensitive and specific detection of functional changes in several disease types. In endocrinology, the precise localisation of small functioning tumours of the pituitary or adrenal glands is crucial for planning curative surgery or radiotherapy. While PET imaging shows good promise for this task, initial studies suggest significant room for improvement, with improved PET imaging and subsequent more accurate localisation opening up the possibility for more adapted therapies. In dementia, the accurate quantification of PET images is key for the early detection of disease. Improved PET imaging may allow for earlier detection of dementia while asymptomatic and increased sensitivity to assess and monitor treatment once appropriate drugs have been found. In this project mathematicians team up with researchers and clinicians from Addenbrooke's Hospital Cambridge, Dementias Platform UK (DPUK), GE Healthcare and University College London (UCL) for improved diagnosis and localization for tumours in endocrinology and earlier diagnosis of dementia with improved PET imaging. In particular, we investigate modern PET reconstruction approaches based on advanced mathematical methods to increase the PET image resolution and contrast, while keeping computational complexity low, thereby directly benefiting clinical workflow.

Changes in the environment inside cells can be considered as alterations in cellular chemistry. The cellular environment can be thought to span a spectrum between reducing conditions (often characterised by a lack of oxygen, and the presence of chemicals that contain hydrogen) and oxidising conditions (often characterised by the presence of oxygen and reactive oxygen-containing species). The spectrum of REDucing to OXidising environment is known as REDOX chemistry. The REDOX environment in the cell results from external stimuli, and affects the function of the cell. Consequently, the REDOX environment can give rise to cellular changes that result in diseases. In this work, we propose that the reverse is also true - that the REDOX state of a cell at a given time will provide predictive information on the fate of a particular cell. Therefore, if it were possible to gain a global picture of the cellular REDOX state, this would be a revolutionary way of predicting cell fate, and hence treating disease. For this new technique to work we need a range of molecular tools that tell us about a given component of the REDOX state at any given time. The aim of our work is to develop and validate tools that detect the intracellular molecules that affect the cellular REDOX state, and provide imaging feedback on that state. By combing the feedback from several of these molecular tools we can infer information on the overall REDOX state. To achieve this aim we have assembled a team of people with the wide range of skills required to make the proposed molecular tools. Our team includes synthetic inorganic and organic chemists, people skilled in a range of imaging techniques, and biological scientists who will be able to apply the molecular tools that we will make. Only by combining the skills of everybody in our team will we be able to achieve the aims of this ambitious, but potentially revolutionary, programme of research.

Imaging plays an important role in many applications in the natural sciences, medicine and the life sciences, as well as in engineering and industrial applications. An example is an MRI image of a brain used by a physician to detect a brain tumour such as glioblastoma. At the core of many imaging applications is an inverse problem, i.e. the mathematical problem of reconstructing the image from data produced by the imaging machine, for example the MRI machine. Such inverse imaging problems have been approached for many years in a "knowledge-driven" way, using information about the device and the imaging procedure. However, the knowledge-driven models cannot always be solved, are computationally very expensive, or deliver suboptimal images. In recent years, new "data-driven" methods, which use past examples of successfully reconstructed images together with the data that produced them, have been shown to produce some impressive results in image reconstruction. The problem with such data-driven methods, however, is that currently they do not have "mathematical guarantees", in other words one cannot state the degree to which the results are reliable. They also have the property that even small deviations in the data could result in large differences in the results. This clearly could have devastating implications for many applications. In this proposal, we will develop a new hybrid approach that combines the best of knowledge-driven and data-driven methods for inverse imaging problems, crucially providing the mathematical guarantees essential to being able to use the methods in real-world applications. Once the challenging task of developing these mathematical methods is achieved, we will apply this learning to produce an imaging pipeline that draws into a single step the stages of the imaging process, thus optimising the process further. We will apply the new methods to real-world applications. For example, using the data driven mathematical methods developed in the project and working closely with the Radiology Department, we will create an end-to-end workflow where multi-modal image acquisition, reconstruction, segmentation and image analyses are performed jointly and optimised for the end task of real time treatment response assessment in patients with metastatic cancer.