Advanced X-Ray Micro-CT Methods for The Delineation of Tumour Heterogeneity in Lung Cancer

1) Brief description of the context of the research including potential impact
Analysis of tumour texture heterogeneity using machine learning techniques with CT-images shows potential for the prediction of cancer malignancy and aggressiveness [1], which may inform patient treatment plans. Phase-contrast x-ray techniques, which derive image contrast from phase-shifts in propagating x-rays [2], provide improved sensitivity to texture and microstructure in samples that would appear mostly homogenous to conventional, attenuation-based x-ray imaging. This project proposes to investigate x-ray phase-contrast micro-CT as a method to image ex-vivo lung samples and produce three dimensional representations. The new approach will be compared to the current histopathological standard, which will be the ground truth. Additionally, machine learning and other advanced computational techniques will be applied for the registration, segmentation, and classification of the images. This work will contribute to the efforts in scaling up these techniques towards new in-vivo clinical applications.
2) Aims and Objectives
The project will entail a bottom-up approach, starting with the modelling, optimisation and fine tuning of the imaging setups. Image retrieval algorithm development will follow, along with an imaging protocol for a pilot study with clinical tissue samples. From both modelling and the pilot data on ex-vivo tissue samples, it will be possible to extrapolate a comprehensive system design and specification for the prospective in-vivo implementation of the technique.

3) Novelty of Research Methodology
The edge-illumination technique has been applied to the phase-contrast x-ray imaging of ex-vivo tissue samples [3][4], with its capabilities extended to tomographic imaging [5]. Building on these milestones, this project will exploit the high-resolution and texture of phase-contrast images to categorise tumour heterogeneity in lung cancer.

4) Alignment to EPSRC's strategies and research areas
This is a strongly interdisciplinary project, aligning with EPSRC themes across the domains of engineering, physical sciences, and healthcare technologies. Key areas of research are medical imaging, sensors and instrumentation, and artificial intelligence technologies.

5) Any companies or collaborators involved
The infrastructure available to this project includes a first commercial prototype X-ray phase-contrast scanner through the UCL-Nikon Prosperity Partnership, and two unique custom-built X-ray

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.