Fully Funded PhD: Real time MRI of joint and muscle mobility in elite football players

The project forms part of an exciting new research and innovation partnership between Manchester Metropolitan University and Manchester United Football Club.

The successful candidate will be embedded across the two institutions, thereby undertaking developmental research on two in-house 3 Telsa magnetic resonance imaging (MRI) scanners. This project aims to develop novel MRI methodologies and analysis software to drive improved health and performance outcomes for players.

You will develop innovative real-time imaging for achieving unparalleled 3D visualization of lower limb joint movements to inform wider exploration of muscle/bone analytics for automatic injury identification.

The successful candidate will have programming experience, in particular training AI models, preferably in an applied imaging/sport setting. In post, you will gain comprehensive training on the operation of clinical MRI scanners. The successful candidate will be responsible for conducting all aspects of the research project, from data collection and analysis to disseminating the findings at scientific conferences and through publication in world-leading journals.

AIMS AND OBJECTIVES

The project aims to deliver AI-empowered innovative dynamic imaging information and key kinetic metric outputs concerning football player mobility (in particular lower limb joint/muscle/bone) for use by Manchester United Football Club. This state-of-the-art approach to real-time imaging will be integrated into an AI imaging analytics package to promote automatic injury detection and aid player diagnosis by the partner medical team.

You will meet this aim by completing the following key objectives:

• Development/programming of real-time dynamic imaging of joint motion (>70 frames per second - based on multi-band radial k-space sampling and sparse reconstruction) in the Canon Sequence Development Environment.
• Implementation of AI models to empower hybrid imaging – merging the benefits of low spatial resolution real-time dynamic information with high-resolution structural imaging.
• Integrating dynamic/hybrid imaging into wider software-based AI imaging analytics of muscle asymmetry, fat infiltration, oedema and scaring.
• Use deep learning to deliver automatic injury detection methodologies to reduce false negative assessments.
• Explore the application of the developed methodologies after injury onset to track recovery metrics following training strategies implemented by the partner medical team.

Your research and development in this imaging space will have an immediate impact on a world-leading football club. In addition, the techniques you devise will find wider application across musculoskeletal research frameworks.