Motion Modelling and Motion Compensated Reconstruction from Cone-Beam CT Projection Data

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1) Brief description of the context of the research including potential impactOn-board Cone-Beam CT (CBCT) imaging systems are available on the majority of Radiotherapy treatment machines, but image acquisition times of ~1 minute make it difficult to reconstruct images showing respiratory motion. This research project will develop methods to model the respiratory motion directly from unreconstructed projection data, and use these models to perform a motion-compensated reconstruction of the CBCT volume. This could potentially lead to more accurate, safer, and more effective treatments for lung cancer patients.2) Aims and Objectives1. Develop, implement, and evaluate 'surrogate-driven' and 'surrogate-free' motion models that can be applied to CBCT projection data.2. Investigate different methods for performing the motion-compensated reconstruction of the CBCT data, including modern iterative and learning based approaches.3. Investigate modern machine learning based approaches for fitting the motion models to the projection data.4. Establish tools and workflows for using the motion models to inform and guide adaptive radiotherapy. 3) Novelty of Research MethodologyInitial work has been undertaken at UCL and elsewhere on fitting motion models directly to projection data, but so far this has not been successfully applied to real patient data and there are a number of challenges still to be overcome. Furthermore, all previous work has focussed on 'surrogate-driven' models. Surrogate-free motion models using a low-rank decomposition approach have recently been proposed for MRI but have not previously been applied to CBCT data. Novel methods will also be investigated for performing the motion compensated reconstructions and for fitting the motion models using modern machine learning approaches. Finally, the motion models will enable novel methods to be developed for adapting radiotherapy treatment to better account for the respiratory motion.4) Alignment to EPSRC's strategies and research areasThis project is aligned with EPSRC's Healthcare technology theme, especially the challenges of Expanding the Frontiers of Physical Intervention and Optimising Disease Prediction, Diagnosis, and Intervention. It is also aligned with the Medical Imaging research area. 5) Any companies or collaborators involvedElekta will be involved in the project.The Christie Hospital and the Institute of Cancer Research will also collaborate on the project by providing CBCT data and expert advice