Technology Overview

A novel approach to deliver a hybrid of anatomical and functional medical imaging.

Working environment


The EMVT technology presents a disruptive imaging modality with potentially far-reaching applications and commercial opportunity. The same types of electromagnetic waves that mobile phones use to transmit voice and data are now being used for medical imaging. It is the product of over a decade of research and development at The University of Queensland. The technology has come out of a research group led by Professors Amin Abbosh and Stuart Crozier at the School of Information Technology and Electrical Engineering. The dynamic team has built a series of advanced prototypes which have led up to the EMVT clinical prototype that is currently in pre-clinical testing. These advancements included an optimized antenna array, innovative signal capture plus image processing algorithms, and machine learning driven boundary conditions and stroke classification.

How It Works

Electromagnetic Microwave Imaging

Electromagnetic microwave imaging provides a different spatial resolution from that of a CT or MRI, with a potential high sensitivity to changes in the electrical properties of tissue which can be influenced by factors such as temperature, blood flow, water content and hypoxia.


The Challenge

To date industry participants have struggled to produce genuine three-dimensional images of biological tissue with microwave tomography in a practical amount of time.

The Breakthrough

A series of highly innovative algorithms that map the dielectic properties of tissue. Using low-power microwave signals, whilst solving the challenges of image quality, accuracy and computational time, to produce three-dimensional images of the brain in a matter of minutes. Read about the team behind the breakthrough here.

The EMVT System

Point of care, non-ionising,
non-invasive, safe, cost effective and rapid





  • Multi-Antenna Crown
  • Multi-Port VNA
  • Standard Laptop




Signal and image processing.

  • Boundary Conditions
  • Anomaly Identification
  • Anomaly Verification
  • Stroke Classification