I completed my PhD as part of the Oxford Nottingham Biomedical Imaging (ONBI) Doctoral Training Program, graduating in 2021. I am now a research fellow at the Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, working on using MRI to assess kidney transplant viability.
Kidney transplantation is the preferred treatment for end-stage kidney disease. However, patients on the transplant list have prolonged haemodialysis with increased morbidity and mortality rates. Donor kidney viability is currently assessed using donor age, medical history, and serum creatinine, but these have limited predictive power, resulting in potentially viable donor organs not being transplanted. I aim to develop a rapid, non-invasive, assessment method of donor kidney quality by scanning the kidney outside the body. This additional information about the viability of a potential transplant organ will give surgeons confident to make use of more donated kidneys.
In addition to my work on assessing transplant kidneys, I also develop open source software/computational methods for the renal MRI community. This has focused on development of AI kidney segmentation algorithms to eliminate the need for slow manual segmentation, thus enabling studies with hundreds of participants. Additionally I work on the UK Renal Imaging Network Kidney Analysis Toolbox (UKAT), a Python package for analysis of renal MRI data.
Modules I have taught/tutored/supervised include:
DANIEL, ALEXANDER J., BUCHANAN, CHARLOTTE E., ALLCOCK, THOMAS, SCERRI, DANIEL, COX, ELEANOR F., PRESTWICH, BENJAMIN L. and FRANCIS, SUSAN T., 2021. Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network Magnetic Resonance in Medicine. 86(2), 1125-1136
DANIEL, ALEXANDER J, NERY, FABIO, SOUSA, JOÃO, BUCHANAN, CHARLOTTE, LI, HAO, PRIEST, ANDREW N, SOURBRON, STEVEN, THOMAS, DAVID L. and FRANCIS, SUSAN T, 2021. UKRIN Kidney Analysis Toolbox (UKAT): A Framework for Harmonized Quantitative Renal MRI Analysis In: Proc. Intl. Soc. Mag. Reson. Med. 29. 2379