Deep-learning PET-MR longitudinal reconstruction for lower-dose antibody-imaging in the understanding and treatment of cancer

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering


Aim of the PhD Project:

This project explores new synergistic multi-modality data with an emphasis on AI-enhanced PET-MR image reconstruction methods, exploiting AI to improve imaging capabilities for cancer treatment monitoring. Methods will improve quantification, enable lower dose scanning and explore analyses that exploit synergies of information-rich longitudinal datasets.

Project Description

This project aspires to the following progress:

Development of advanced longitudinal PET image reconstruction algorithms, which are able to draw benefit from each and every longitudinal multi-modality scan of the subject under study
Utilisation and co-modelling not only of the multiple PET datasets but also the longitudinal multi-contrast / multi-parametric MR data, with a view to direct multi-parametric synergistic PET-MR reconstruction from the rich multi-modality datasets
Exploitation of deep learning methods to arrive at new state of the art longitudinal, multi-modal, multi-parametric and multi-data synergistic image reconstruction methods
We have already made initial advances in these image reconstruction methodologies, but this project will further develop and unify these advances, and importantly also for the first time embed the power of deep learning into longitudinal and multi-parametric reconstruction. We anticipate that using deep-learned longitudinal image reconstruction for multi-modal and multi-parametric imaging will result in more robust antibody imaging, delivering enhanced image metrics for greater capabilities in cancer imaging and personalised treatment planning and monitoring.

Planned Impact

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022104/1 30/09/2019 30/03/2028
2440754 Studentship EP/S022104/1 30/09/2020 29/03/2022 Maxwell Buckmire-Monro