Background and overall aims:

Deep Brain Stimulation (DBS) entails the surgical implantation of miniature electrode arrays deep within the brain. These electrodes are connected to a pacemaker-like neurostimulator implanted under the skin in the chest. Similar to the way a pacemaker jolts the heart to keep rhythm, the neurostimulator provides pulses of electricity to the brain to suppress abnormal activity. This therapy has been used for over two decades and has resulted in remarkable outcomes for patients.

One of the most important aspects of DBS surgery is the accurate placement of the electrodes. The neurosurgeon must carefully guide the electrode array (1.3mm diameter) deep within the brain to a target which is about 5mm in length – the size of a pea. Being just a millimetre off-target reduces treatment efficacy and leads to unwanted side-effects (such as slurred speech). Consequently, poorly positioned electrodes are often removed and (if possible) re-positioned during a second surgery. Neurosurgery is not without risk and second revision surgery is avoided at all costs.

The neurosurgical team relies on medical imaging (MRI and CT) to plan electrode trajectories (avoiding major blood vessels and regions of importance) as well as to verify final placement once implanted. This process is subjective, requiring a highly experienced neurologist and neurosurgeon familiar with brain anatomy to determine the target site and judge implant accuracy. This approach is inadequate for scientific endeavours, which require far more stringent reporting. An exact measure of targeting accuracy is needed in situations where we may consider the difference between two surgical techniques.

At present, there are several manual processes or assumptions required to undertake neuroimaging analysis:

  1. The orientation of the images must be rotated to conform to a standardised coordinate system
  2. The neurosurgical target within the brain must be labelled on an individual basis
  3. An assumption is made that the electrode array has not rotated axially (e.g. due to torsion) after implantation

This research project will aim to address each of the limitations noted above.

General methods to be used in the project:

  • Algorithm development (many existing algorithms written in C++)
  • Data science (Python or equivalent)
  • Digital signal processing (particularly image processing; co-registration and normalisation techniques)
  • Mathematics (Matrices, Transformations, Vector operations)
  • Statistics and machine learning
  • Data visualisation (2D/3D)
  • Clinical trial execution and management

 Suitable Background of Students:

This project would suit an electronics or biomedical engineer with a background in programming, data science, and mathematics. Previous experience with image processing and neuroimaging analysis will be highly regarded. Strong communication skills (verbal and written) must be demonstrated and a willingness to work in a flexible environment within a multidisciplinary team is crucial.

For all enquiries please email [email protected]

Supervisors: A/Prof Kate Fox (RMIT), Dr Thushara PereraA/Prof James Fallon