How would you explain your research?

Healthy, ‘normal’ brain structure as documented by anatomical atlases and informed by brain models, can be applied to identify pathology. Understanding the anatomic variations within the brain is a main focus in Neuroimaging to produce effective and high-accuracy brain segmentations. Segmenting the brain sub-structures without taking into account anatomical differences leads to segmentation errors created by imaging artefacts and limited spatial resolution.

Current Deep Learning applications in neuroimaging are not informed by detailed information on brain anatomy, relying instead on a limited sets of labelled training examples. This leads to model failure.  My project aims to counteract the issues associated with using limited training data sets in brain anatomy modelling, by producing an explicit model of healthy brain anatomy and embedding it into a Deep Learning algorithm, wich can be trained by using the plentiful available unlabelled data on healthy brains which have become readily available.

Why Nottingham and why the Precision Imaging Beacon?

The pioneering role of Nottingham University in the development of MRI, alongside the collaborative approach of the Beacon, QMC and SPMIC- with the multi-disciplinary team of mathematicians, clinicians, engineers and physicists, attracted me to complete my PhD at Nottingham. Furthermore, as my PhD is split between the University of Nottingham and University of Adelaide, the link between the universities and subsequent benefits of working between two internationally leading research teams is extremely exciting.

What inspired you to pursue this area?

I have witnessed first-hand the impact Neurological conditions can have on individuals and their family, and this has formed my desire to complete medical research in an aim to improve or reduce its often-devastating effects. Understanding how technology can benefit healthcare, and the importance of consistently improving imaging methodologies and post-processing techniques to not only diagnose pathology but guide treatment outcome, drives my passion for Neuroimaging analysis. Furthermore, after being consistently drawn to clinically relevant modules in my undergraduate degree, and utilising medical imaging analysis software to develop translational tools in projects, I realised that I could contribute to the field of Neuroscience as an engineer.  

 Current Deep Learning applications in neuroimaging are not informed by detailed information on brain anatomy, relying instead on a limited sets of labelled training examples. This leads to model failure. 

How will your research affect the average person?

By producing a model that takes into consideration anatomical structure of the brain for neuroimaging analysis, we can improve interpretability, sensitivity (i.e. ability to detect pathology) and specificity (i.e. reduce number of false findings) of resulting outputs. This will aid in the studying of the healthy brain and identification of brain pathologies as anomalies. If utilised by clinicians, the research will enable patients scans to be more accurately analysed through automated segmentation, and aid in the identification of pathology progression or initiation, to subsequently improve patient outcome.

What’s been the greatest moment of your career so far?

 Achieving a first-class in my Masters degree in ‘Mechanical Engineering with Biomechanics’ was probably my greatest achievement so far, as it provided the stepping stone I needed to be awarded a Joint PhD scholarship by the University of Adelaide, in order to complete my PhD between the Beacon at the UoN and School of Computer Science at the University of Adelaide. However, I think the greatest moment of my career so far was probably when I realised that I wanted to do a PhD whilst working on my own project during an medical engineering work placement, as I thrived off the independence of driving my own clinically focussed research project.

How will being based at UoN and joining Precision Imaging help you achieve your goals?

Being part of the Precision Imaging Beacon means that my work will be guided by academics who specialise in neuroimaging, developing new imaging methods and analysis techniques. This will be particularly relevant for me, as I my project will need the support of a multidisciplinary team- and as I am starting from a less typical background of Mechanical Engineering, the support of a collaborative environment in which I can draw on the experiences of others within the Beacon, will be highly beneficial. 

What aspects of your research and role are you looking forward to?

I am looking forward to driving my own project based off my own research and developing my technical and research skills to suit what I think is necessary for my project. This will be the first time I have had this amount of freedom in my own research and training, so I am looking forward to the independence of designing my own schedule and subsequently planning my own learning. I am also looking forward to working between two research institutions and teams of researchers on opposite sides of the world, as I feel that having two groups of perspectives on my project will be highly beneficial for my research and enhance my personal development.