Quantitative Imaging Group

We are researching methods for quantifying blood-brain barrier (BBB) damage using MRI; these methods may be useful in characterising a wide range of neurological conditions, including dementia, epilepsy, multiple sclerosis, and tumours. Dynamic contrast-enhanced MRI (DCE-MRI) is one technique that can quantify BBB damage, particularly when there are relatively high levels of leakage (severe damage). We are developing new water exchange methods to measure the rate at which water moves between the blood and tissue across the BBB, which should enable quantification of more subtle alterations that occur, for example, at disease onset (low-level damage).

We use MRI to study lung structure and function and how they are affected by disease. Oxygen-enhanced MRI measures how well oxygen is delivered to the lungs in conditions such as cancer, COPD, asthma, and cystic fibrosis. Hyperpolarised xenon MRI images lung air spaces and shows how easily gas moves into the blood.

Our relaxometry research focuses on measuring three key magnetisation relaxation times that underpin MRI signals: T1, T2, and T2*.
For the blood–brain barrier (BBB), we study the longitudinal relaxation time T1, which can reveal BBB disruption. We measure T1 using Magnetic Resonance Fingerprinting (MRF), a time-efficient technique that estimates multiple MRI parameters within a single scan. In the brain, we investigate the transverse relaxation time T2, a marker of hippocampal tissue change in epilepsy with potential for broader neuroradiological applications. Our work evaluates rapid, model-based T2 mapping methods suitable for clinical use. For the lungs, we study T2*, which reflects magnetic field susceptibility and helps quantify lung function in our oxygen-enhanced MRI research.

Our research group also develops artificial intelligence (AI) methods for medical image analysis. One area of focus is ensuring these tools can reliably indicate their level of certainty, which is essential for their safe use in clinical settings. In parallel, we investigate the use of AI to better understand and replicate contrast enhancement in brain tumour MRI scans without the need for contrast injections. By combining standard MRI with advanced image analysis, this work aims to make brain imaging safer, more accessible, and more sustainable.