Enhanced FEM-Based DBIM Approach for Two-Dimensional Microwave Imaging

Lu, Pan; Córcoles, Juan; Kosmas, Panagiotis

doi:10.1109/tap.2020.3044806

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…We have developed and validated a computationally efficient 3-D DBIM algorithm for microwave head imaging. Our DBIM implementation uses the FISTA solver for the linear inverse problem, which has shown advantages over traditional CGLS solvers in our previous work [ 58 ]. The 3-D DBIM-FISTA algorithm relies on an in-house 3-D FDTD forward solver implemented on GPU, which is equally accurate but runs considerably faster than previous implementations with commercial software Acceleware.…”

Section: Discussionmentioning

confidence: 99%

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

Kosmas

2022

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

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

Kosmas

2022

Sensors

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“…The quality of the recovered images by this scheme is better than that of the DBIM, and the computation time is also significantly reduced. The multi-frequency (MF) technique has been studied and applied to the DBIM in [25][26][27][28][29][30][31][32][33][34][35][36][37]. This approach is applied as follows: in the first step, the lower frequency acquired data are used to ensure fast convergence, and in the second step, the higher frequency acquired data are used to ensure the high resolution of the reconstructed image.…”

Section: Methodsmentioning

confidence: 99%

High Sound-Contrast Inverse Scattering by MR-MF-DBIM Scheme

et al. 2022

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In ultrasound tomography, cross-sectional images represent the spatial distribution of the physical parameters of a target of interest, which can be obtained based on scattered ultrasound measurements. These measurements can be obtained from dense datasets collected at different transmitter and receiver locations, and using multiple frequencies. The Born approximation method, which provides a simple linear relationship between the objective function and the scattering field, has been adopted to resolve the inverse scattering problem. The distorted Born iterative method (DBIM), which utilizes the first-order Born approximation, is a productive diffraction tomography scheme. In this article, the range of interpolation applications is extended at the multilayer level, taking into account the advantages of integrating this multilayer level with multiple frequencies for the DBIM. Specifically, we consider: (a) a multi-resolution technique, i.e., a multi-step interpolation for the DBIM: MR-DBIM, with the advantage that the normalized absolute error is reduced by 18.67% and 37.21% in comparison with one-step interpolation DBIM and typical DBIM, respectively; (b) the integration of multi-resolution and multi-frequency techniques with the DBIM: MR-MF-DBIM, which is applied to image targets with high sound contrast in a strongly scattering medium. Relative to MR-DBIM, this integration offers a 44.01% reduction in the normalized absolute error.

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“…The distorted Born iterative method with the two-step iterative shrinkage/thresholding (DBIM-TwIST) linear inverse solver has been proposed in [43]. Recently, the algorithm has been used to reconstruct images from experimental or numerical three-dimensional (3-D) data [44], and it has been tested with both finite-difference time-domain (FDTD) as well as finite element method (FEM)-based forward solvers [45].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of the DBIM-TwIST Algorithm for Brain Stroke Detection and Differentiation Using a Multi-Layered Anatomically Complex Head Phantom

Karadima

Sotiriou

et al. 2022

IEEE Open J. Antennas Propag.

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We present an experimental validation of the distorted Born iterative method with the two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm for the problem of brain stroke detection and differentiation, using an anatomically accurate, multilayer head phantom. To this end, we have developed a gelatine-based, anatomically complex head phantom which mimics various brain tissues and also includes a target mimicking hemorrhagic or ischemic stroke. We simulated the model and setup using CST Microwave Studio and then used our experimental imaging setup to collect numerical and measured data, respectively. We then used our DBIM-TwIST algorithm to reconstruct the dielectric properties of the imaging domain for both simulated and measured data. Results from our CST simulations showed that we are able to locate and reconstruct the permittivity of different stroke targets using an approximate initial guess. Our experimental results demonstrated the potential and challenges for successful detection and differentiation of the stroke targets.

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Enhanced FEM-Based DBIM Approach for Two-Dimensional Microwave Imaging

Cited by 6 publications

References 26 publications

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

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

High Sound-Contrast Inverse Scattering by MR-MF-DBIM Scheme

Experimental Validation of the DBIM-TwIST Algorithm for Brain Stroke Detection and Differentiation Using a Multi-Layered Anatomically Complex Head Phantom

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