Evaluation of prior information in microwave tomography experiments for brain stroke detection

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

This work examines the impact of prior information (or 'initial guess') for calibrating a microwave tomography system for brain stroke detection and differentiation, using a multi-layered, anatomically complex head phantom. The imaging algorithm applies the distorted Born iterative method (DBIM) combined with the two-step iterative shrinkage thresholding (TwIST) method. The initial guess for the algorithm is based on two models with different available information: one filled with the dielectric properties of average brain tissue, and one with a more accurate representation of the true head phantom. Our initial results demonstrate that the addition of thin head tissue layers (such as CSF) in the forward model is not critical for the successful reconstruction of the target's dielectric properties. As expected, however, we achieve more accurate results with the multi-layer initial guess in challenging cases such as detecting an ischemic stroke-like target in the presence of a six-layers Zubal head phantom.

Original languageEnglish
Title of host publication2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages535-539
Number of pages5
ISBN (Electronic)9781728196978
DOIs
Publication statusPublished - 2021
Event2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021 - Antibes Juan-les-Pins, France
Duration: 15 Nov 202117 Nov 2021

Publication series

Name2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021

Conference

Conference2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021
Country/TerritoryFrance
CityAntibes Juan-les-Pins
Period15/11/202117/11/2021

Keywords

  • brain stroke
  • calibration
  • DBIM
  • microwave tomography
  • prior information
  • TwIST

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