Imaging Keynote Theatre 2

Tuesday 17th March

Wednesday 18th March


11.00 - 11.30

Larry Sitka

Incorporating AI in the Imaging Workflow

Big Data/BIG Data Analytics Data Consolidation, bring all relevant information to the point of care, in a single viewer in the context of the patient and use this knowledge to affect outcome. Redefining PACS: The three main components of PACS – Viewer, Workflow, Archive – are not going away, they are just being pulled apart and reimagined.


11.45 - 12.15

Matthew Clemence, PhD

Introduction to machine learning and AI in the Healthcare and Imaging environment

Machine learning and AI methods are set to have a significant technological and social impact from both the patient and healthcare delivery specialists. This seminar will present both the historical context and an overview of the methods for non-specialists looking at how these techniques may impact MRI imaging, radiology and also touch upon the ethical questions which need to be addressed.


12.30 - 13.00

Dr Qaiser Malik

Radiology Improvement and Transformation

Efficiency and Transformation in Radiology. Using Lean methods to improve the performance. Using innovation to advance practice. Role of AI in the future of Radiology.


13.30 - 14.00

Iris Grunwald

How To Set Up An Interventional Stroke Service Using AI and High-Fidelity Simulation

The seminar will provide a practical guide on how to set up an interventional stroke service of excellence.


14.00 - 14.30

Dr Ai Lyn Tan

How imaging has enhanced knowledge about rheumatic diseases

The presentation will highlight the use of MRI and ultrasound as a research tool in improving the understanding of the pathogenesis of arthritides including inflammatory arthritis such as rheumatoid and psoriatic arthritis, and osteoarthritis. Introduction to how imaging using quantitative MRI techniques and ultrasound shear wave elastography can be used to explore the impact of rheumatic diseases on muscles.


14.45 - 15.15

Dr Raj Jena

Building AI workflows for segmentation and analysis of medical imaging

Project InnerEye focuses on the use of machine learning models for automatic segmentation of medical images. Segmentation can form the basis of treatment planning in radiotherapy and surgery, or a tool that enables quantification of longitudinal images and radiomics workflows. I present examples of research algorithms and tools designed to accelerate these clinical workflows.


15.30 - 16.00

Professor Ian Marshall

Developments in neuro MRI: more information, faster

MRI has traditionally been regarded as a versatile but ‘slow’ imaging methodology. Advances in computing power and algorithms are now addressing the speed issue, enabling collection of a wider range of clinically useful information in an acceptable examination time. The seminar will describe these developments in the context of neuroimaging applied to neurodegenerative and cerebrovascular diseases.