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

1) Brief description of the context of the research including potential impact

On-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 Objectives

1. 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 Methodology

Initial 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 areas

This 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 involved

Elekta 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

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.