Highly Accelerated Magnetic Resonance Angiography using Deep Learning

Imaging of the blood vessels (angiography) is particularly important in the brain, where disturbances in blood supply and haemorrhage have severe consequences. Angiograms allow the visualisation of both blood supply disruption (atherosclerosis, embolism) and vascular abnormalities (aneurysms, arteriovenous malformations). However, conventional angiographic techniques require the injection of a contrast agent and exposure to ionising radiation, resulting in some risks to the patient.
Certain magnetic resonance imaging (MRI)-based methods do not have these drawbacks: for example, time-of-flight (TOF) and arterial spin labelling (ASL) angiography. ASL is less well established than TOF, but improves vessel visibility and allows dynamic, vessel-selective angiograms to be obtained. However, both suffer from long scan times, particularly when high spatial resolution and whole-head coverage are required, making them difficult to fit into busy clinical protocols and increasing the likelihood of image corruption due to patient motion.
In this project highly accelerated angiographic methods will be developed that combine undersampling in the raw signal (k-space) domain with novel image reconstruction methods based on physics-informed supervised deep learning. We anticipate that the very specific branching structure of vessels within the brain can be well represented by deep convolutional networks, allowing rapid scans with high spatial resolution and whole-head coverage without the artefacts and long processing times associated with conventional (parallel imaging or compressed sensing) undersampled reconstructions.
This will involve optimising unrolled iterative network architectures in combination with undersampling patterns, training and testing on large retrospectively undersampled TOF datasets, fine-tuning for ASL angiography, and application to prospectively undersampled rapid scan data in healthy volunteers and patient cohorts. This framework will also be adapted for vessel identification, allowing quantitative analysis of the acquired angiographic data (e.g. branching patterns, vessel tortuosity) and providing crucial information for complementary MRI-based pulse wave velocity measurements of arterial stiffness, thought to be implicated in vascular dementia.