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

Abstract

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.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022104/1 01/10/2019 31/03/2028
2440754 Studentship EP/S022104/1 01/10/2020 30/09/2024 Maxwell Buckmire-Monro