Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
Summary
This project will involve simulation and test of an impedance spectroscopy device using electrical impedance models and saline based phantoms for its applicability in monitoring stroke and associated
therapies.
Supervisor(s)
Professor Alistair McEwan, Associate Professor Craig Jin, Professor Philip Leong
Research Location
Electrical and Information Engineering
Program Type
Masters/PHD
Synopsis
Stroke is one of the leading causes of death in the western world. Recently ‘clot-busting’ thrombolytic drugs have become available for when the stroke is associated with a blockage or ischemia, however
bleeding or haemorrhage must be excluded before therapy can commence. There are several ongoing trials of new treatments for acute stroke at RPA and The George Institute for International Health. In order to assess new treatments and manage dosage of thrombolytic drugs, a device to continuously monitor bleeding in the brain would be a great advantage. This project will develop a good potential candidate: Electrical Impedance Tomography Spectroscopy which is a relatively new, portable medical imaging technology based on impedance measurements using scalp electrodes.
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Keywords
Biomedical Instrumentation, Signal Processing, Medical Electronics, Stroke
Opportunity ID
The opportunity ID for this research opportunity is: 948
Other opportunities with Professor Alistair McEwan
- Medical diagnostics for neonates in the developing world
- Impedance tomography for cardiac imaging: high speed tomography
- Novel Electrodes for rapid electrophysiological recording
- Implant electrode optimisation and neurolinguistics
- Subdivided electrodes to improve defibrillators and physiological measurements
- Electrical impedance modelling for implants
- Magnetic resonance electrical impedance tomography
- Development of a microwave catheter for cardiac ablation to treat ventricular tachycardia
- Resuscitation monitors for the neonatal intensive care uni (NICU)
- ARTIFICIAL INTELLIGENCE/MACHINE LEARNING ASSISTED NEWBORN CRITICAL CARE (VIRTUAL CRITICAL CARE MODELING)
- Mapping 2D Images to 3D Shape
- New technique for studying human brain activity
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Statistical models of ear shape and ear acoustics
- Binaural signal processing algorithms for hearing aids
- FPGA-based low latency machine learning
Other opportunities with Associate Professor Craig Jin
- Pattern analysis techniques for sound synthesis
- Interpolation of binaural impulse responses for virtual auditory displays
- Sound field recording and recreation
- Beamforming with acoustic vector sensors - Multiple acoustic source localisation using acoustic vector sensor arrays
- Speech separation and localisation using particle filtering
- Mapping 2D Images to 3D Shape
- New technique for studying human brain activity
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Statistical models of ear shape and ear acoustics
- Binaural signal processing algorithms for hearing aids
- Impedance tomography for cardiac imaging: high speed tomography
- Medical diagnostics for neonates in the developing world
- Novel Electrodes for rapid electrophysiological recording
- FPGA-based low latency machine learning
Other opportunities with Professor Philip Leong
- FPGA-based low latency machine learning
- FPGA control of photonic systems
- Mapping 2D Images to 3D Shape
- New technique for studying human brain activity
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Statistical models of ear shape and ear acoustics
- Medical diagnostics for neonates in the developing world
- Impedance tomography for cardiac imaging: high speed tomography
- Novel Electrodes for rapid electrophysiological recording
- Binaural signal processing algorithms for hearing aids