Patient-Specific Modelling of Intraoperative Infusion Dynamics Using Time-Resolved MRI

About the Project

We are offering a fully funded PhD scholarship to develop patient- and tissue-informed models of intraoperative gadolinium (Gd) infusion dynamics using a unique, time-resolved intraoperative MRI dataset from a human clinical trials study. The dataset comprises ~40 intraoperative scans, each containing 60–100 MRI frames captured during Gd infusion, enabling detailed measurement of how infusate spreads through brain tissue in real time.

Each subject includes multiple infusion trajectories (in each hemisphere), with known and varying flow rates, and systematic variation in infusion depth and spatial location. Infusion sites are precisely localised using stereotactic coordinates, and imaging is provided in DICOM space.

PhD Focus

The successful candidate will work at the Cardiff University Brain Research Imaging Centre (CUBRIC), under a supervisory team comprising Professor William Gray (GrayWP@cardiff.ac.uk); Dr Marco Palombo (PalomboM@cardiff.ac.uk); Dr Leandro Beltrachini (BeltrachiniL@cardiff.ac.uk) or Dr Daniel Gallichan (GallichanD@cardiff.ac.uk).

CUBRIC is a multidisciplinary environment, housing physicists, engineers, neuroscientists, psychologists and clinicians. The student will access state-of-the-art imaging technology, including unique MRI scanners, and get training and hands-on experience with instrument use for human data collection. The successful candidate will also work closely with an established modelling group in the Department of Mathematics, integrating pre-operative microstructural MRI as an input to existing fluid spreading models and the clinical neurosurgical scientists performing gene therapy delivery in clinical trials. A key extension will be incorporating the effects of small vessels on infusate propagation, moving toward mechanistic, tissue-informed predictions.

Research Aims and Expected Outputs

You will:

• Generate time-resolved contours of the infusate, capturing the spatiotemporal evolution of Gd distribution across trajectories.
• Model relationships between infusion parameters (flow rate, depth, trajectory) and resulting spread patterns.
• Quantify inter- and intra-subject variability in infusion dynamics.
• Link pre-operative microstructural properties of the putamen to subsequent intraoperative spread, assessing predictive value for planning.
• Extend modelling to account for microvascular features, improving biological realism.