Open PhD Position in Deep Learning applied to MRI imaging of carotid plaques

Code: 2023TCD03
Title: Deep Learning for Magnetic Resonance Quantitative Susceptibility Mapping of carotid plaques
Supervision Team: Caitríona Lally, TCD (Primary Supervisor) / Catherine Mooney, UCD (External Secondary Supervisor) / Brooke Tornifoglio, TCD and Karin Shmueli, UCL (Additional Supervisory Team Members)
Description: Carotid artery disease is the leading cause of ischaemic stroke. The current standard-of-care involves removing plaques that narrow a carotid artery by more than 50%. The degree of vessel occlusion, however, is a poor indication of plaque rupture risk, which is ultimately what leads to stroke.
Plaque mechanical integrity is the critical factor which determines the risk of plaque rupture, where the mechanical strength of this tissue is governed by its composition. Using machine learning approaches and both in vitro and in vivo imaging, and in particular Quantitative Susceptibility Mapping metrics obtained from MRI, we propose to non-invasively determine plaque composition and hence vulnerability of carotid plaques to rupture.
This highly collaborative project has the potential to change diagnosis and treatment of vulnerable carotid plaques using non-ionizing MR imaging which would be truly transformative for carotid artery disease management.