Three-Dimensional Microwave Head Imaging with GPU-Based FDTD and the DBIM Method

Pan Lu*, Panagiotis Kosmas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

We present a preliminary study of microwave head imaging using a three-dimensional (3-D) implementation of the distorted Born iterative method (DBIM). Our aim is to examine the benefits of using the more computationally intensive 3-D implementation in scenarios where limited prior information is available, or when the target occupies an area that is not covered by the imaging array’s transverse planes. We show that, in some cases, the 3-D implementation outperforms its two-dimensional (2-D) counterpart despite the increased number of unknowns for the linear problem at each DBIM iteration. We also discuss how the 3-D algorithm can be implemented efficiently using graphic processing units (GPUs) and validate this implementation with experimental data from a simplified brain phantom. In this work, we have implemented a non-linear microwave imaging approach using DBIM with GPU-accelerated FDTD. Moreover, the paper offers a direct comparison of 2-D and 3-D microwave tomography implementations for head imaging and stroke detection in inhomogenous anatomically complex numerical head phantoms.

Original languageEnglish
Article number2691
JournalSENSORS
Volume22
Issue number7
Early online date31 Mar 2022
DOIs
Publication statusPublished - 1 Apr 2022

Keywords

  • distorted Born iterative method (DBIM)
  • finite-difference time-domain (FDTD)
  • graphic processing unit (GPU)
  • inverse scattering
  • microwave imaging

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