This PhD studentship is part of the Marie Curie Network FairCFD.

Flow-MRI (magnetic resonance imaging) is a non-invasive imaging method that visualizes fluid flows in the body in 4D (3 spatial and 1 time dimension) without using ionizing radiation. It holds great promise for comprehensive characterization of blood velocity, particularly in the heart and major blood vessels, but is currently hindered by low signal-to-noise ratio (SNR) and low spatial resolution. Scans typically take 30 to 90 minutes because low SNR images must be averaged to generate high SNR images that can be interpreted. Even then, processed images remain noisy, particularly at the vessel walls. This is where accurate flow velocity information is particularly important because the wall shear stress is thought to be a major contributor to cardiovascular disease.

We have developed and tested a method (adjoint-accelerated Bayesian inference) that, in 3D steady flows, reduces engineering Flow-MRI acquisition times by a factor of 100 whilst simultaneously providing accurate wall shear stress and pressure gradient information. This method is promising, but it needs to be extended to pulsatile (periodic) flows through compliant (flexible) boundaries before it can achieve its potential in clinical Flow-MRI.

The objectives of the proposed study are to (i) extend adjoint-accelerated Bayesian inference of Flow-MRI data to 4D pulsatile flows within compliant boundaries; (ii) implement, test, and validate the results with compliant test objects in MRI machines; (iii) increase the image resolution and the predictive accuracy of derived information such as pressure gradients and wall shear stress, and (iv) assess the clinical relevance of this information by working with clinicians.