Optimising Neuroimaging Biomarkers for Dementia Using Deep Learning

1) Brief description of the context of the research including potential impact

Dementia is a leading cause of global morbidity and mortality, yet successful treatments are scarce and mechanistic understanding is incomplete. Neuroimaging plays a key role in our understanding and treatment of dementia, and many biomarkers reflecting the brain's health have been proposed. However, these neuroimaging biomarkers are currently limited by poor generalisability, inadequate robustness to varying types and quality of data, slow computation speeds and arbitrary processing methods. Despite the many potential benefits of neuroimaging biomarkers, these limitations have hindered their use in clinical trials and clinical practice.

2) Aims and Objectives

This project aims to use emerging deep-learning techniques to overcome the above limitations. It will involve the analysis of magnetic resonance imaging (MRI) biomarkers, including whole-brain volume, hippocampal volume, ventricle size, cortical thickness, and white matter lesions. New image segmentation pipelines, based on deep learning, will be developed to generate these biomarkers. The goal is to demonstrate increased reliability, validity, and robustness of these biomarkers for use in dementia research and clinical trials.

3) Novelty of Research Methodology

The project involves developing novel neural-network methods, such as synthetic image augmentation on T1-weighted and T2-weighted MRI scans using generative networks, to enhance the robustness to varying types and quality of data. This research will also explore emerging network architectures, especially vision transformers which applies attention mechanism to differentially weighting the significance of each part of the neuroimaging data. These new image analysis pipelines will be validated using various local and public dementia MRI datasets to establish whether the resulting biomarkers increase sensitivity to disease effects, better predict disease progression and treatment response.

4) Alignment to EPSRC's strategies and research areas

EPSRC aims to "Transform Health and Healthcare" and develop "Artificial Intelligence (AI), Digitalisation and Data". Population ageing exposes more people to the risk of dementia. This project could help clinicians have a better understanding of dementia, allow patients to have a more timely and accurate diagnosis, and provide more supportive evidence in the development of dementia treatments. The project will explore different neural network architectures and methods in MRI analysis, which align well with two research areas in EPSRC - medical imaging and artificial intelligence, as part of the Healthcare Technologies theme.

5) Any companies or collaborators involved

None

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.