Brain tumour and epilepsy are among the leading causes of childhood chronic illness-related death in Australia and worldwide. Brain surgery is the mainstay treatment that reduces disease burden and prolongs survival. Surgery performed near functional brain regions and the inter-connecting nerve fibre tracts has a high morbidity risk, particularly in paediatrics due to the dynamically developing brain.

Advanced MRI-based nerve fibre tract imaging (tractography) has been a key imaging development that assists surgeons in the pre-operative (before surgery) planning and intra-operative (interactively, during surgery) stages. Accurately mapping fibre tracts can greatly assist with post-surgical functional preservation by avoiding surgical tract injuries. Existing commercial surgical tractography software is based on outdated techniques producing anatomically inaccurate tractography image reconstructions that urgently need updating. However, tractography processing takes a long time, relying heavily on a neuroanatomical expert to manually process imaging data, making it infeasible in acute brain surgery settings.

The aim of this project is to develop and implement an automated surgical tractography technique leveraging neurosurgical knowledge and machine learning. To do this, the student will leverage locally and internationally collected multi-modal MRI data and build an automated AI-based tractography model. Neurosurgeons from local and international sites will evaluate the anatomical accuracy and surgical utility of the tractography, to help validate and improve the model.

This project will suit a student that has technical knowledge in computational neuroscience or engineering, and a keen interest in learning how to apply machine learning techniques for use in clinical imaging and neurosurgery.

This project is a collaboration between the Neuroscience Advanced Clinical Imaging Service (NACIS) at the Royal Children’s Hospital, the Developmental Imaging Group MCRI (https://www.mcri.edu.au/research/research-areas/clinical-sciences/developmental-imaging), and the University of Melbourne. The project would suit an individual with a background in computational neuroscience or engineering (or similar) background, with a keen interest in applying machine-learning approaches in clinical imaging and neurosurgery.

If you are interested in this PhD project, send an email to Dr Joseph Yang (primary supervisor, joseph.yang@mcri.edu.au) explaining why you would like to be considered for this opportunity and how the project fits with your experience and interests. Make sure to attach your CV and transcripts to the email. Only enquiries sent via email will be considered.

Dr

Sila Genc

Honorary (Senior Fellow)

Department of Paediatrics

Dr

Sila Genc

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Sila Genc

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