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

TY - JOUR

T1 - Three-Dimensional Microwave Head Imaging with GPU-Based FDTD and the DBIM Method

AU - Lu, Pan

AU - Kosmas, Panagiotis

N1 - Funding Information: Funding: This research was funded in part by UK’s Engineering and Physical Sciences Research Council (EPSRC) under project grant EP/R013918/1. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022/4/1

Y1 - 2022/4/1

N2 - 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.

AB - 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.

KW - distorted Born iterative method (DBIM)

KW - finite-difference time-domain (FDTD)

KW - graphic processing unit (GPU)

KW - inverse scattering

KW - microwave imaging

UR - http://www.scopus.com/inward/record.url?scp=85127096990&partnerID=8YFLogxK

U2 - 10.3390/s22072691

DO - 10.3390/s22072691

M3 - Article

AN - SCOPUS:85127096990

SN - 1424-8220

VL - 22

JO - SENSORS

JF - SENSORS

IS - 7

M1 - 2691

ER -