Machine learning augmented hierarchical tomographic image reconstruction of human organs

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

Imaging of organs at multiple resolutions can be extremely useful for understanding their function, especially if we can correlate cellular level information to whole organ processes. X-ray tomography is one of the methods that can be used to image at cellular level information; however, for a scan of an intact organ, normally this can only be achieved when the sample is small such as mouse organs (millimetres in size). For human organs, traditionally only biopsies could be scanned at such resolution. These biopsies distort, making correlation of the high-resolution results to low-resolution images challenging.
A new technique, Hierarchical Phase-Contrast Tomography (HiP-CT) has been co-developed by UCL and ESRF, that can achieve cellular level resolution for volumes of interest (VOI) in an intact organ, starting from a full organ scan at lower resolution and hierarchically zoom into VOI at higher resolutions. This made it possible to have an intact organ and zoom in at any part to study its cellular level functions. (see human-organ-atlas.esrf.eu and mecheng.ucl.ac.uk/hip-ct)
The tomographic images obtained by HiP-CT provide information across a range of scales. This project will apply and develop machine learning algorithms use the information from all scan resolutions to obtain better feature extraction than can be obtained from a single resolution. If successful, this could enable super-resolution imaging, with a final goal of informing clinical radiology.

2) Aims and Objectives

The aim of the project is to apply and develop machine learning algorithms to assist the reconstruction of high-resolution VOIs using data from lower resolution scan of the entire organ.
The objectives are as follows:
To investigate the use of generative model such as super resolution and generative adversarial nets to assist the reconstruction of VOI.
To investigate the use of unsupervised learning algorithms to identify feature points to be used in the reconstruction process.
To investigate the use of one model for all organ reconstructions or different models for different organs or organ groups.
Considering the possible limitation on data variation, to investigate potential use of data from other sources to help identify key features via unsupervised learning algorithms.
To compare the reconstruction results from the machine learning models with the reconstruction results obtained using the reference scan method.

3) Novelty of Research Methodology

HiP-CT has only been recently developed, the only method to remove noise during the reconstruction process for extreme off-axis scans is via a reference scan and traditional denoising algorithms. The use of machine learning techniques to reconstruct utilising data from a lower resolution scan is yet to be investigated.

4) Alignment to EPSRC's strategies and research areas

The project falls within EPSRC research area of engineering. The project aims to develop machine learning techniques to assist the reconstruction of HiP-CT scans, which helps towards the understanding of human organ structures. It aligns with the EPSRC's strategic priorities of artificial intelligence, digitisation and data, and also transforming health and healthcare.

5) Any companies or collaborators involved

Dr. Paul Tafforeau from the European Synchrotron Radiation Facility (ESRF).

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.