Title
Three-Dimensional Microwave Head Imaging with GPU-Based FDTD and the DBIM Method
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.
Year
DOI
Venue
2022
10.3390/s22072691
SENSORS
Keywords
DocType
Volume
distorted Born iterative method (DBIM), microwave imaging, finite-difference time-domain (FDTD), graphic processing unit (GPU), inverse scattering
Journal
22
Issue
ISSN
Citations 
7
1424-8220
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Pan Lu100.68
Panagiotis Kosmas200.34