Hybridizable Discontinuous Galerkin Method Contrast Source Inversion of 2-D and 3-D Dielectric and Magnetic Targets

Brown, Kevin G.; Geddert, Nicholas; Asefi, Mohammad; LoVetri, Joe; Jeffrey, Ian

doi:10.1109/tmtt.2019.2905214

Cited by 22 publications

(9 citation statements)

References 44 publications

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“…as a forward solver to support distinct high-order expansions for the fields, contrasts and contrast sources, but in a full 3D vector-field implementation. Further details on both 2D and 3D DGM-CSI can be found in [38,[57][58][59]. Figure B.1: First stage scenario depicted for a multilayer synthetic phantom in air containing fat (yellow), fibroglandular tissue (blue, "Fibro" = fibroglandular), and a tumour (red).…”

Section: B31 Dgm-csi Algorithmmentioning

confidence: 99%

“…Implementations of CSI based on finite-element method (FEM) forward solvers and more recently, the time-harmonic discontinuous Galerkin (DG) formulation of Maxwell's curl equations, have proven effective when modelling complicated imaging environments such as resonant chambers, and the use of FEM-CSI and DGM-CSI in many two-dimensional and three-dimensional4.2. Introduction87imaging scenarios, for dielectric and magnetic targets, has been extensively studied[48,[57][58][59]92].While conventional MWI continues to advance towards clinical application as a low-cost complementary imaging tool, a competitive need to improve the modality's limited spatial and contrast resolution has steered research towards microwave contrast media. Such contrast enhancement is a relatively new extension to the field, and since changes in relative permeability (µ r ) have been largely ignored for biomedical applications due to the intrinsically non-magnetic environment of the human body, most of the studies that have been carried out have focused primarily on agents that modify the relative permittivity (ε r ) of the targeted tissues[26,27,47].…”

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confidence: 99%

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Enhanced Detection of Magnetic Nanoparticles Using a Novel Microwave Ferromagnetic Resonance Imaging System

Kaye

Gilmore

LoVetri

2021

IEEE Trans. Biomed. Eng.

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Although microwave imaging (MWI) remains a promising future diagnostic tool in breast cancer detection and monitoring, its progress towards widespread clinical application would greatly benefit from an improvement to its relatively low spatial and contrast resolution in delineating healthy and abnormal tissues, especially in comparison to established modalities such as mammography and magnetic resonance imaging (MRI). To this end, research has been undertaken in this work exploring multiple methods of improving the quality of MWI reconstructions.

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Section: B31 Dgm-csi Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

Enhanced Detection of Magnetic Nanoparticles Using a Novel Microwave Ferromagnetic Resonance Imaging System

Kaye

Gilmore

LoVetri

2021

IEEE Trans. Biomed. Eng.

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“…We assume that the breast can be placed inside the chamber within a support that would fix the ROI boundary within the imaging chamber ( Figure 1). A full-wave 3-D model of this system is constructed using either of our in-house FEM [14] or DGM [12] solvers, the latter of which supports high-order geometry simplifying modeling of curved regions [38]. These models output H-fields at specified receiver points for each transmitter, where we can convert between S-parameters and H-fields through calibration (details of the transmitter and receiver probe numerical models can be found in [38].…”

Section: Faceted Air-filled Chamber and Its Model For Microwave Breasmentioning

confidence: 99%

“…The synthetic data used for training and evaluating the network was generated using a high-order Discontinuous Galerkin Method (DGM) forward solver [12,38]. In all cases, second-order mesh geometry and third-order field solutions were used.…”

Section: Data Generationmentioning

confidence: 99%

“…During this time improvements to algorithms have been made possible by efficient 3-D forward solvers. For example, parallel finite-element method (FEM) or discontinuous Galerkin method (DGM) based algorithms have been applied to realistic biomedical imaging scenarios for breast cancer monitoring [12][13][14] and stroke detection [15]. Microwave imaging, both in 2-D and 3-D, has also benefited from advancements in understanding and using prior information [16,17], including hard and/or soft prior from another modality [7,18,19], another algorithm applied to the same data [20], or knowledge of the expected materials [21,22].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

et al. 2021

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A two-stage workflow for detecting and monitoring tumors in the human breast with an inverse scattering-based technique is presented. Stage 1 involves a phaseless bulk-parameter inference neural network that recovers the geometry and permittivity of the breast fibroglandular region. The bulk parameters are used for calibration and as prior information for Stage 2, a full phase contrast source inversion of the measurement data, to detect regions of high relative complex-valued permittivity in the breast based on an assumed known overall tissue geometry. We demonstrate the ability of the workflow to recover the geometry and bulk permittivity of the different sized fibroglandular regions, and to detect and localize tumors of various sizes and locations within the breast model. Preliminary results show promise for a synthetically trained Stage 1 network to be applied to experimental data and provide quality prior information in practical imaging situations.

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Stored Grain Inventory Management Using Neural-Network-Based Parametric Electromagnetic Inversion

et al. 2020

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We present a neural network architecture to determine the volume and complex permittivity of grain stored in metal bins. The neural networks output the grain height, cone angle and complex permittivity of the grain, using the input of experimental field data (S-parameters) from an electromagnetic imaging system consisting of 24 transceivers installed in the bin. Key for practical applications, the neural networks are trained on synthetic data sets but generate the parametric information using experimental data as input, without the use of calibration objects or open-short-load measurements. To accomplish this, we formulate a data normalization scheme that enables the use of a loss function that directly compares measured S-parameters and simulation model fields. The normalization strategy and the ability to train on synthetic data means we do not need to collect experimental training data. We demonstrate the applicability of this synthetically trained neural network to experimental data from two different bin geometries, and discuss the ability of these neural networks to successfully infer parameters that can be used for grain inventory management. Our neural-network-based approach enables rapid inference, providing a more cost-effective long-term solution than existing optimization-based parametric inversion methods.

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Hybridizable Discontinuous Galerkin Method Contrast Source Inversion of 2-D and 3-D Dielectric and Magnetic Targets

Cited by 22 publications

References 44 publications

Enhanced Detection of Magnetic Nanoparticles Using a Novel Microwave Ferromagnetic Resonance Imaging System

Enhanced Detection of Magnetic Nanoparticles Using a Novel Microwave Ferromagnetic Resonance Imaging System

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

Stored Grain Inventory Management Using Neural-Network-Based Parametric Electromagnetic Inversion

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