About the Project
Half of all cancer patients have some form of radiotherapy (RT) treatment, resulting in >130k external beam RT episodes pa in England. Patient motion during RT, e.g. due to respiration and peristalsis, remains a main confounder of accurate treatment delivery, causing overdosing of organs-at-risk or underdosing of tumour, and leading to poorer survival outcomes or greater post-treatment morbidity. If this motion can be predicted during treatments, we can design treatment adaption strategies to compensate for it, and correspondingly improve patient outcomes. This project aims to create fundamentally new approaches to modelling such motions using so-called physics-informed neural networks (PINNS).
Neural network-based simulation approaches can offer huge speed-ups compared with conventional computational models (e.g. finite element models). They also offer a means of directly linking model predictions with low-level patient data like images, potentially streamlining clinical use. Compared with standard neural networks, PINNs incorporate physical constraints within their loss functions to ensure network predictions correctly reflect relevant physical, and in this case physiological, laws. While improving the reliability of model predictions, this also can massively reduce the amount of training data required, and make the resulting networks more interpretable – both features of very high importance in medical applications.
1) Formulate physics-based biomechanical models of respiratory and peristaltic motion incorporating image-derived anatomy, advanced tissue constitutive models, and robust interaction with surrounding anatomy; validate using phantom data and 3D/4D MR/CT image-derived measurements.
2) Develop Physics-Informed-Neural-Network approaches that accelerate model predictions and link predictions directly with patient images.
3) Evaluate model effectiveness within RT treatment adaption scenarios: pilot study on inter-fraction motion-compensated dose accumulation and treatment plan adaptation using model predictions.
This project is inherently interdisciplinary. It correspondingly provides an opportunity to develop high-level skills in several distinct areas:
· Quantitative skills: the project has computation and machine-learning at its core. As part of your doctoral training programme, you will access an array of formal modules in these areas. You will also access related seminar programmes and journal clubs run by CISTIB, LIDA, IMBE, and others within the University. Simulation and machine-learning are the foundation of emerging disciplines like computational medicine. You will finish your degree well-equipped to pursue cutting edge research in this and many other areas.
· Interdisciplinary skills: the project sits at the engineering/medicine interface. You will receive close guidance and training in clinical and medical physics aspects of RT practice, and clinical implementation/translation of advanced imaging and AI technologies.
You will be based in the Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB – co-led by Dr Taylor) at the University of Leeds. You will have strong links also with the Clatterbridge Cancer Centre in Liverpool, and the Leeds Cancer Centre. More information on these centres and the project supervisors can be found here:
Benefits of being in the DiMeN DTP:
This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.
We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.
Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: http://www.dimen.org.uk/overview/student-profiles/flexible-supplement-awards
Further information on the programme and how to apply can be found on our website:
Studentships commence: 1st October 2021
2) SF Johnsen, et al. (2015): NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics, Int J Comput Assist Radiol Surg, 10:1077-1095. https://doi.org/10.1007/s11548-014-1118-5
3) JC Vardakis, et al. (2019): Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection. Morphologie, 103: 148-160. https://doi.org/10.1016/j.morpho.2019.10.045
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