Alex Daniel

Contact

• workRoom A3 Sir Peter Mansfield Imaging Centre
  University Park
  Nottingham
  NG7 2RD
  UK
• work0115 84 66881
• Alex.Daniel@nottingham.ac.uk

Biography

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.

Expertise Summary

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.

Teaching Summary

Modules I have taught/tutored/supervised include:

• Nottingham Medical Imaging Academy (NMedIA) Image Analysis module
• NMedIA Computing module
• Machine Learning in Science MSc projects

Selected Publications

• 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., COX, ELEANOR, BUCHANAN, CHARLOTTE and FRANCIS, SUSAN, 2020. A Comparison of T2 Mapping Methods in the Kidneys In: Proc. Intl. Soc. Mag. Reson. Med. 28.
• DANIEL, ALEXANDER J., COX, ELEANOR, BUCHANAN, CHARLOTTE, BRADLEY, CHRISTOPHER and FRANCIS, SUSAN, 2018. Applying T2 Relaxation Under Spin Tagging (TRUST) to assess renal oxygenation in the kidney In: Proc. Intl. Soc. Mag. Reson. Med. 26.
• 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

• View all publications

• BUCHANAN, CHARLOTTE E, LI, HAO, NERY, FABIO, DANIEL, ALEXANDER J, SOUSA, JOAO DE, SOURBRON, TEVEN, PRIEST, ANDREW, THOMAS, DAVID and FRANCIS, SUSAN T, 2021. Harmonisation of Multiparametric Renal MRI for Multi-Centre Studies In: Proc. Intl. Soc. Mag. Reson. Med. 29. 2402
• 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
• 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., COX, ELEANOR, BUCHANAN, CHARLOTTE and FRANCIS, SUSAN, 2020. A Comparison of T2 Mapping Methods in the Kidneys In: Proc. Intl. Soc. Mag. Reson. Med. 28.
• DANIEL, ALEXANDER J., BUCHANAN, CHARLOTTE, ALLCOCK, THOMAS, SCERRI, DANIEL, COX, ELEANOR, PRESTWICH, BENJAMIN and FRANCIS, SUSAN, 2020. Automated Renal Segmentation in Healthy and Chronic Kidney Disease Subjects Using A Convolutional Neural Network In: Proc. Intl. Soc. Mag. Reson. Med. 28.
• KAZMI, ISMA, DANIAL, ALAXANDER, BUCHANAN, CHARLOTTE, FRANCIS, SUSAN, MAARTEN W, TAAL and NICK, SELBY, 2019. Determining Optimal Technique for Tissue Fixation for Ex-Vivo MRI Imaging of Porcine Kidneys Nephrology Dialysis Transplantation. 34(gfz106.FP093),
• DANIEL, ALEXANDER J., BUCHANAN, CHARLOTTE, KAZMI, ISMA, COX, ELEANOR, SELBY, NICHOLAS, GARDNER, DAVID and FRANCIS, SUSAN, 2019. The Effects of Fixation and Age on Relaxometry Measurements of Ex-Vivo Kidneys In: Proc. Intl. Soc. Mag. Reson. Med. 27.
• DANIEL, ALEXANDER J., SMITH, JAMES A., SPENCER, GLYN S., JORGE, JOÃO, BOWTELL, RICHARD and MULLINGER, KAREN J., 2019. Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI Human Brain Mapping. 40(2), 578-596
• DANIEL, ALEXANDER J., COX, ELEANOR, BUCHANAN, CHARLOTTE, BRADLEY, CHRISTOPHER and FRANCIS, SUSAN, 2018. Applying T2 Relaxation Under Spin Tagging (TRUST) to assess renal oxygenation in the kidney In: Proc. Intl. Soc. Mag. Reson. Med. 26.
• DANIEL, ALEXANDER J, BUCHANAN, CHARLOTTE, MORRIS, DAVID M., LI, HAO, NOBLE, REBECCA, SOUSA, JOÃO, SOURBRON, STEVEN, THOMAS, DAVID L., PRIEST, ANDREW N and FRANCIS, SUSAN T, Generalisability of Automated CNN-based Renal Segmentation for Multi-Vendor Studies In: Proc. Intl. Soc. Mag. Reson. Med. 30. 2160