Professor Michael Chappell did an MEng in Engineering Science in Oxford (1999-2003) before gaining a doctorate in the same department (2003-2006) using mathematical models to explore the growth of bubbles from dissolved gases under decompression in the body - a topic of particular relevance to SCUBA divers, where the resulting sickness is commonly referred to as 'the bends'. Subsequently, he moved to the Oxford Centre for Functional MRI of the Brain (2006-2009) as a Post-Doctoral Research Associate.
Before his current appointment, Michael was a Senior Research Associate in the Centre of Excellence in Personalized Healthcare (2009-2012), based at the Institute of Biomedical Engineering.
Advanced methods for imaging acute stroke patients on admission to hospital.
Perfusion imaging in dementia
Developing methods to increase the sensitivity and specificity of Arterial Spin Labelling MRI to detect changes in perfusion associated with early stages of dementia.
Software tool for visualising and analysing physiological imaging data.
Software tools for the analysis of perfusion information from Arterial Spin Labelling MRI (part of the FMRIB Software Library)
Oxford Neuroimaging Primers, M. Chappell & M. Jenkinson (Eds.), OUP
The Oxford Neuroimaging Primers are short texts aimed at new researchers or advanced undergraduates from the biological, medical or physical sciences. They are intended to provide a thorough understanding of the ways in which neuroimaging data can be analyzed and how that relates to acquisition and interpretation.
Each primer has been written so that it is a stand-alone introduction to a particular area of neuroimaging, and the primers also work together to provide a comprehensive foundation for this increasingly influential field.
Physiology for Engineers, M. Chappell & S. Payne, Springer.
Physiology for Engineers is an introduction to qualitative and quantitative aspects of human physiology. Covering a number of biological and physiological processes and phenomena, including a selection of mathematical models, showing how physiological problems can be mathematically formulated and studied.
It illustrates how a wide range of engineering and physics topics, including electronics, fluid dynamics, solid mechanics and control theory can be used to describe and understand physiological processes and systems.
I welcome applications from students who would like to undertake study in image analysis and Bayesian inference with application in quantitative physiological medical imaging