Tissue engineering - aimed at developing "lab-grown" organs and tissue by combining appropriate scaffolds and cells - could solve one of the biggest medical problems of our times, the shortage of donor organs. While the pool of scaffold materials is large (e.g. natural/synthetic biomaterials), there is consensus that the extracellular matrix (ECM) of the target tissue is an excellent choice as it possesses native structural and biomechanical properties. ECMs can be derived from cadaver tissue (e.g. from animals) through a process called decellularization, by which the tissue undergoes several cycles of flushing with detergents and enzymes. A successfully decellularised tissue is characterised by the absence of cellular material and the presence of an intact ECM. Imaging, for assessing the ECM, is an extremely important tool for the development of decellularisation methods that are simultaneously gentle and effective. This project is about developing a new imaging tool for characterising decellularised tissue based on x-ray micro computed tomography (CT). Since micro-CT is a non-destructive technique, the inspected samples can be used further in longitudinal studies or be implanted into animals to test their performance in vivo. In comparison, the current gold standard techniques for inspecting ECMs (histology, electron microscopy) require that samples are sliced, sectioned and/or stained in preparation for being imaged, prohibiting using them in any further studies. A number of substantial developments will be needed before micro-CT can become a valuable tool for validating decellularisation techniques and other methodologies in tissue engineering. Currently, micro-CT fails to meet the complex imaging needs of this field, which often requires multi-scale and multi-contrast approaches. First, a micro-CT machine with zooming in capabilities would be required to inspect the multi-level structure of ECMs. Second, decellularised tissue generally exhibits weak x-ray attenuation; hence, the micro-CT machine should provide access to phase contrast alongside attenuation contrast, which is known to provide a much better visualisation of tissue scaffolds than the latter. The micro-CT machine proposed here will have both these functionalities. It will exploit an innovative imaging mechanism that is underpinned by the idea to structure the x-ray beam into an array of narrow (micrometric) beamlets via a mask placed immediately upstream of the sample. This provides flexibility in terms of spatial resolution, as this metric - unlike in conventional micro-CT scanners - is not defined by the blur of the source and detector. Instead, resolution is driven by the beamlet width, which can be made smaller than the intrinsic system blur, bearing unique potential for fast resolution switching and multi-scale imaging. Second, it provides access to complementary contrast channels (phase, ultra-small angle x-ray scattering). These channels result from small x-ray photon deviations which occur alongside attenuation when x-rays interact with matter. While most conventional micro-CT scanners are blind to these effects, the machine proposed here will enable their detection, allowing to reconstruct three sets of complementary tomographic images for each sample. While the phase channel can provide a much higher contrast-to-noise ratio than the attenuation channel, the ultra-small angle x-ray scattering channel encodes the presence of sub-resolution features in a sample. The latter bears unique potential for image-guided zooming in. The project will culminate in the design, construction and test of an experimental prototype for image-guided multi-scale and multi-contrast imaging with a field of view of up to 10 cm by 10 cm, which may be expanded to larger dimensions in the future. A broad range of decellularised tissues will be scanned, and the results benchmarked against the current gold standard (histology or electron microscopy).

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.