Intelligent image-driven motion modelling for adaptive radiotherapy
Tuesday 13th August 2019
CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine
A fundamental challenge for personalised radiotherapy (RT) is enabling regular in-treatment RT plan adaption to account for motion of organs. This motion may be associated with everything from respiration and peristalsis to bladder/bowel filling, and even coughing. In each case, its effect is to diminish the accuracy with which RT can be delivered to target regions (tumours), meaning that significant treatment margins must be incorporated in the plans, with the certainty that normal tissues also will then receive elevated levels of radiation exposure. If tissue motions in individuals during treatment can be characterised reliably, RT plans may be adapted correspondingly, potentially allowing dose escalation in target regions, while simultaneously reducing toxicity in sensitive nearby tissues. To this end, reliable and regularly updated per-patient models of these motions are required, along with robust and efficient techniques for driving such models with intra-treatment patient measurements – most importantly, images.
The project’s overarching aim, therefore, is formulation of image-driven modelling approaches that enable real-time estimation of tissue motion states from sparse and low quality images acquired during treatments. We envisage a hybrid modelling approach for this purpose that integrates biomechanical models of the relevant organs with artificial intelligence (AI) techniques that can learn mappings between model configurations and corresponding images. This will ensure motion predictions are based on reliable physical and physiological principles, while enabling robust and efficient exploitation of information derived from images.
The work will thus involve:
- Development of patient-specific computational (biomechanical) models of organ motion, based on finite element methods or other suitable techniques.
- Formulation of innovative AI methods that link model predictions with image-derived observations.
- Development of automated methods and tools for generating individual patient motion models from routine patient clinical data (images etc).
The work will be based within CISTIB, at the University of Leeds, but will involve close collaboration with researchers from the Leeds Cancer Centre, based at St James’s hospital. As can be seen, the project will require a genuinely cross-disciplinary approach that spans (at least) traditional disciplines of computational biomechanics, data science, and medical imaging.
It presents an opportunity for a motivated and able candidate to develop expertise in all three of these areas, while also gaining experience of close working with clinical collaborators and of the challenges associated with translating technical solutions into the clinic.
The successful candidate will have an excellent first degree in Engineering, Mathematics, Computer Science, or a related discipline. Strong mathematical skills are essential, as is expertise and experience in one or more of the following technical areas: biomechanical/ physiological modelling; artificial intelligence and machine learning; and medical image computing. Well-developed programming skills are also important, ideally with experience of efficiently implementing complex algorithms in the context of one or more of the mentioned technical areas. Knowledge of radiotherapy techniques, and of oncology more generally are not essential, but would be valuable. Experience of working in a research environment, and any corresponding track record of publishing results in journals and conferences, are similarly non-essential but valued.
Contact Zeike Taylor for further information or an informal discussion: email@example.com.
Closing date: 1st September 2019
|Organization||CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine|
|Location||University of Leeds, Leeds, UK|
|Title||Intelligent image-driven motion modelling for adaptive radiotherapy|