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Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke

Behnaz Sohani1, James Puttock1, Banafsheh Khalesi1

  • 1School of Engineering, London South Bank University, London SE1 0AA, UK.

Sensors (Basel, Switzerland)
|October 1, 2020
PubMed
Summary

This study compares microwave imaging artefact removal techniques for detecting simulated brain haemorrhages. Specific methods were evaluated in a realistic phantom, showing improved stroke detection accuracy.

Keywords:
Huygens principleUWB imagingartefact removal methodsbrain stroke detectionmicrowave imagingportable medical devices

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Electromagnetics

Background:

  • Ultra-wideband Microwave Imaging (MWI) offers potential for non-invasive tissue characterization.
  • Artefacts in MWI can obscure important diagnostic information, hindering clinical application.
  • Effective artefact removal is crucial for improving the accuracy of MWI-based diagnostics, such as stroke detection.

Purpose of the Study:

  • To investigate and quantify the performance of various artefact removal methods for ultra-wideband Microwave Imaging.
  • To evaluate these methods in a realistic phantom environment simulating human head tissue and brain haemorrhage.
  • To compare the efficacy of different artefact removal techniques for enhancing stroke detection in MWI.

Main Methods:

  • Utilized an ultra-wideband Microwave Imaging device employing multi-bistatic scattering measurements and Huygens principle-based imaging.
  • Developed a two-layered phantom with a cylindrical inclusion to mimic brain haemorrhage.
  • Applied and compared several artefact removal algorithms to MWI data acquired with the inclusion at multiple positions.

Main Results:

  • Successful detection of the simulated brain haemorrhage was achieved after applying artefact removal methods.
  • Quantitative metrics were used to compare the effectiveness of different artefact removal techniques against a reference image.
  • The study demonstrated the impact of artefact removal on improving image quality and diagnostic potential in MWI.

Conclusions:

  • Artefact removal methods significantly enhance the detection capabilities of Microwave Imaging for simulated intracranial haemorrhages.
  • Quantitative evaluation provides a basis for selecting optimal artefact removal strategies for MWI applications.
  • This research contributes to the advancement of MWI as a viable tool for stroke diagnosis.