Informing 4D flow MRI haemodynamic outputs with data science, mathematical models and scale-resolving computational fluid dynamics

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In the UK, heart diseases cause around a quarter of all deaths and related healthcare costs are estimated at £9 billion annually. The aorta is the largest artery in the body, connected directly to the heart. Aortic disease is one of the most common types of cardiovascular disease and can be extremely life threatening. Wall shear stress is the shearing force exerted by blood flow on the inner surface of the arterial wall and is an important biomarker for arterial wall diseases. Similarly, blood flow disturbances in the blood like turbulence are also linked with heart disease. 4D flow magnetic resonance imaging (MRI) is a type of MRI sequence which measures blood flow velocities in arteries and is used both in clinic and in cardiovascular research. In clinic, MRI is used to diagnose heart disease and evaluate treatments. Currently, important metrics like wall shear stress and turbulence cannot be accurately evaluated from 4D flow MRI, although it may be possible with the development of new methods.In this research fellowship I will develop new methods to radically improve the haemodynamic output capabilities of 4D flow MRI. This will be achieved using computational fluid dynamics, data science and machine learning methods. MRI sequences have various settings determined by the user and currently, settings are optimised for velocity field acquisition, not wall shear stress or turbulence. To enable accurate measurement these parameters, optimal MRI settings need to be established. I will develop a tool for 'virtual' 4D flow MRI which can replicate real 4D flow MRI sequences. The tool will then be used to optimise 4D flow MRI sequences virtually. Next, I will develop a super resolution-based machine learning model which can improve 4D flow MRI haemodynamic outputs. The model will be trained using a combination of high-resolution and low-resolution computational fluid dynamic simulations of various aortas. Developed models will be validated using new multi-resolution 4D flow MRI scans and high-resolution computational fluid dynamic aorta simulations.This research will be developed via collaboration with experts and state-of-the-art facilities found within the Department for Mechanical, Aerospace and Civil Engineering and the Division of Cardiovascular Sciences at the University of Manchester. The multidisciplinary project will draw expertise from researchers, clinicians and life scientists in the UK, as well as make use of MRI facilities at the BHF Manchester Centre for Heart & Lung Magnetic Resonance Research enabling new scan acquisitions. Broadly, this research will advance data-driven fluid dynamics techniques and MR imaging methods, directly impacting healthcare sectors as well as benefitting fluid dynamics, data-science and cardiovascular research communities. In research, improved 4D flow MRI would enable larger-scale studies and likely improve computational fluid dynamic studies informed with MRI data. In clinical settings, access to a wider range of haemodynamic parameters from MRI would enable development of new diagnostic tools and new treatment tools for aortic disease, improving patient outcomes. At the end of the project, I will have developed novel methods to improve 4D flow MRI outputs at different stages - from MRI acquisition through to image post-processing.