Matt graduated with an MPhys in Physics from the University of Sheffield in 1998 and a PhD in Maths from Imperial College in 2002 where he worked on statistical mechanics and biological evolution with Prof. Henrik Jensen. He then spent a couple of years working for a small City of London consultancy designing bespoke logistics optimisation software for commercial and government clients in the UK and Denmark. He started work in MRI research in 2004 at St George’s, University of London before moving to UCL where he developed biophysical simulations and image analysis methodologies, primarily in diffusion MRI. He then spent two years working for the UK financial regulator before returning once again to research, this time at UCL’s Institute of Child Health, where he still holds a joint position. He joined NPL in 2017 and is developing the MRI thread within the medical physics group.
MRI is a complex problem from an imaging standardisation perspective – there are many ways of forming images with different types of contrast, and scanners at different sites are often optimised again different sets of requirements – there are a lot of ways to adjust a scanner. This is challenging, particularly for multi-site studies such as clinical trials or population studies is it means that images acquired at different sites have different statistical properties. This is also a challenge for new advanced analysis methods, such as machine learning. We’re investigating new test objects to allow scanner differences to be quantified, collaborating with UK industry and colleagues in clinical science to develop new QA and calibration procedures. We’re also working closely with our colleagues in Data Science to understand and quantify uncertainties in MR measurements, covering the complete imaging pipeline, from table to image formation, to post-processing and inference. We’re also working with our national and international partners to align all this with larger similar efforts elsewhere.
Email Matt Hall