I am currently involved with the EPSRC Grand Challenges Project: Translating biomedical modelling into the heart of the clinic. This project aims to show for the first time how complex computational models, made patient specific via medical imaging and integrated with other clinical information, can be used to tackle a major problem in healthcare. This project involves many researchers from numerous universities and hospitals in the UK across a range of skills and disciplines.
Most of my current work is to do with finding ways of getting better information we can get from three-dimensional ultrasound images of the heart (known as echo images). In particular, we are looking at how to use a technique known as image compounding to improve the images acquired from an echo acquisition. My primary collaborators for this work are Dr Graeme Penney, Cheng Yao and Dr John Simpson.
Prior to joining King's, I spent the last few years as a post-doctoral research associate and Engineering Doctorate (EngD) research student, at UCL's Centre for Medical Image Computing (CMIC) and the Department of Computer Science. My research focused on modelling and reconstructing complex sub-voxel white matter structure in the human brain using diffusion MRI, and I was supervised by Prof. Daniel Alexander, who leads the Microstructure Imaging Group. During my time at UCL, I was also involved with the diffusion MRI toolkit Camino, and I ran the demonstration labs for Image Processing, as well as the Introduction to Computer Science course for postgrad students.
My first degree was a BA in Computer Science from Emmanuel College, University of Cambridge. I went on to study the Master of Research (MRes) in Computer Vision, Image Processing, Graphics and Simulation course (now known as the MSc CGVI) at UCL's Department of Computer Science before starting the EngD.
My general fields of interest are Image Processing, Medical Imaging, Computer & Machine Vision, Pattern Recognition, Computational Modelling, Brain Modelling, Heart Modelling, Diffusion MRI, Echocardiography and Ultrasound.