Towards quantitative image analysis of respiratory imaging data for pulmonary disease assessment.

The proposed research encompasses new generation of computational algorithms and applied research in cancer image analysis that will impact not only the medical imaging field, but primarily contribute toward a significant understanding of cancer, treatment planning and response assessment. While the main focus here is on lung tumours, many of the concepts being explored have applicability beyond (e.g. liver diseases). The developed computational algorithms will be capable of learning automatically, building on available data, but weak-supervision (either coming from our a priori knowledge or human interaction when necessary) will be also available to provide biomedically plausible outcomes. Therefore, it is expected that the new generation algorithms can also be applied in other fields of engineering and mathematics that make use of imaging for instance in computational modelling.

This research project goes substantially beyond the state-of-the-art in medical image analysis in term of:

- a methodology by a novel paradigm extending current local or global formulation to a unified non-local counterpart considering all components of image registration (similarity measure, motion/deformation model, optimisation method) jointly with quantitative imaging;
- a hypothesis that this unified image analysis framework will be physiologically explicitly grounded in the analysed data (potential to link with advances in machine learning, and opening up to future opportunities for collaboration on big data), and be consequently more relevant to biomedical applications.
- challenge on improving the performance of motion estimation, the author believes that the presented methodological advances will eventually lead to the development of real-time (close to real-time) motion estimation for highly dimensional biomedical data. Such an advance will have immediate impact beyond personalised precise radiotherapy planning and delivery.