Speaker
Description
Eliana Vasquez Osorio is a Reader in Computational Radiotherapy at the University of Manchester and an Honorary Researcher at The Christie NHS Foundation Trust. She trained as a computer scientist at Universidad EAFIT in Medellín, Colombia, and completed her PhD in Medical Physics at Erasmus University Rotterdam, the Netherlands. Her research develops computational and AI methods to make radiotherapy safer, more personalised and better understood, with a particular focus on reirradiation. Her work combines medical image computing, image registration, dose mapping, uncertainty quantification, outcome modelling and causal inference, with a particular interest in the development and safe clinical translation of AI-based methods for radiotherapy.
Summary
Deformable image registration (DIR) is a cornerstone of modern radiotherapy workflows, supporting applications such as treatment adaptation, reirradiation and dose accumulation. This lecture will review how deep learning is transforming the field, from augmenting individual components of conventional iterative registration algorithms to enabling fully end-to-end implementation. Current capabilities, remaining limitations, and key challenges for clinical translation will be discussed, with a focus on what is still needed for AI-driven DIR to achieve meaningful impact in radiotherapy.