Guanbo Chen

A Conformal FDTD Method With Accurate Waveport Excitation and S-Parameter Extraction

We demonstrate a conformal finite-difference time domain (CFDTD) technique with accurate waveport excitation and S-parameter extraction. We discuss, under the CFDTD framework, object modeling with effective subcell material, impedance calibration for the conformal modeled coaxial feed, waveport mode calculation with a newly developed conformal 2-D finite-difference frequency domain solver, waveport excitation with a modified total field scattered field method, and a modified S-parameter extraction scheme that is compatible with the CFDTD method. Lastly, we present two specific validation cases intended asWe demonstrate a conformal finite-difference time domain (CFDTD) technique with accurate waveport excitation and S-parameter extraction. We discuss, under the CFDTD framework, object modeling with effective subcell material, impedance calibration for the conformal modeled coaxial feed, waveport mode calculation with a newly developed conformal 2-D finite-difference frequency domain solver, waveport excitation with a modified total field scattered field method, and a modified S-parameter extraction scheme that is compatible with the CFDTD method. Lastly, we present two specific validation cases intended as relevant precursors to the inverse scattering application: modeling of the reflection coefficient of a tapered patch antenna and modeling of the transmission coefficient in a probe-fed rectangular waveguide. relevant precursors to the inverse scattering application: modeling of the reflection coefficient of a tapered patch antenna and modeling of the transmission coefficient in a probe-fed rectangular waveguide.

Advances in real-time non-contact monitoring of medical thermal treatment through multistatic array microwave imaging

In this paper, we present some recent advances in real-time non-contact monitoring of thermal treatment through multistatic array imaging. The work presented was primarily motivated by the need to improve the flexibility and computational efficiency of the forward modeling of microwave imaging systems in general and of our real-time microwave thermal monitoring system in particular. Specifically, we have developed a conformal finite-difference time domain (CFDTD) solver that addresses several limitations inherent to our previously reported forward modeling methods. In particular, this CFDTD solver has enabled the implementation of a fully integrated numerical vector Greens function that addresses the task of linking the objects scattered field to the measured scattered voltage in a more general and flexible way than the waveport vector Greens function method used in our earlier work. Both the CFDTD solver and the integrated numerical Greens function are validated for a prototype microwave imaging cavity through comparison with CST Microwave Studio. These validated methods are currently being used in an ongoing experimental characterization of microwave imaging for both dielectric reconstruction and real-time thermal monitoring.

An optimized GPU-accelerated FDTD method for microwave imaging using a fast nonlinear inverse scattering algorithm

Summary form only given: Microwave imaging has received considerable attention as a low cost, non-invasive, non-ionizing method for breast cancer detection. In previous work, we have presented a time-domain nonlinear inverse scattering algorithm with multiparameter optimization for microwave imaging. In order to apply this algorithm to an experimental system that we have developed, it is crucial to have an accurate forward model of the imaging cavity. In this presentation, the modeling of the cavity using an in-house GPU accelerated finite-difference-time-domain (FDTD) method will be introduced, demonstrating several optimizations for increased computational efficiency and accuracy. S-parameter simulations of the cavity antennas comparing the results with the commercial software packages Ansys HFSS and CST MWS will be shown. Finally, results from microwave imaging tests of our GPU accelerated inversion algorithm using this fast forward model for both breast cancer detection and for real-time thermal monitoring of focused hyperthermia will be presented.The imaging cavity is a dodecagon consisting of 12 panels. Each panel has three dual-band bow-tie patch antennas operating at 915MHZ and 2.1GHz. In order to accurately capture the fine geometry of the cavity, we have utilized a nonuniform orthogonal mesh. The electrical field grid distance varies slowly in each direction, while the magnetic field resides in the middle of two adjacent electrical field. Though in this scenario the electrical field no long resides in the middle of two adjacent magnetic field points, which may result in first-order error locally, it has been shown by Monk1 that second-order error can still be achieved globally. In addition, we exploit the fact that within one Yee cell, the electrical field and magnetic field in each direction are half grids away to create an anisotropically filled Yee grid. This implementation maintains the accuracy of the cavity model with reduced grids and thus reduced cost of computation.

Real-time tracking of metallic treatment probe in interstitial thermal therapy

In this paper, we propose an inverse scattering method with compressive sensing to image and track the metallic ablation probe during the interstitial thermal therapy. The contrast source inversion (CSI) method is used to solve the inverse scattering problem, which determines the location of the probe by utilizing the scattered field data produced by the contrast source current at the probe surface. A fast spectral gradient-projection method is used to solve the linear inverse problem with sparsity constraints and reconstructs the surface profile of the metallic probe. The proposed method will first be validated numerically by imaging a PEC probe in a realistic interstitial thermal therapy model, and then be validated experimentally with a vector network analyzer based inverse scattering measurement system.

Numerical Vector Green’s Function for S-Parameter Measurement With Waveport Excitation

We present a numerical vector Green’s function that directly links the object material property within the electric field volume integral equation (VIE) to the scattered S-parameters. This Green’s function is particularly useful for microwave inverse scattering applications in which S-parameter measurements are used to reconstruct the dielectric properties of unknown objects in an inhomogeneous background where the Green’s function does not have an analytic form. Unlike the dyadic Green’s function in a standard VIE, which relates the fields in the object domain (vector) to the scattered fields (vector), this vector Green’s function kernel relates the object domain fields (vector) to the measured S-parameters (scalar). Also this new vector Green’s function uses a waveport source method, which accurately accounts for the measured scattered S-parameters between antenna feeds of arbitrary impedance and modes. A detailed derivation is presented, and simulated and experimental examples are provided to validate this vector Green’s function for the prediction of scattered S-parameters.

FDTD based numerical Green's function for S-parameter measurement in inverse scattering problems

We present a numerical Greens function that serves as the kernel for volume integral equation (VIE) in inverse scattering problems. The primary motivation for this research is to address the lack of full antenna modeling in the VIE when solving inverse scattering problems. The numerical Greens function introduced here directly links the total field in the object domain and the objects dielectric properties to the measured scattered S parameters in the presence of the antennas and a presumed imaging cavity.

A preclinical system for focused microwave thermal therapy with integrated real-time 3D microwave thermal monitoring

The use of focused microwave thermal therapy as an adjuvant to radiatior and chemotherapy for breast cancer treatment continues to be an active a of research with the aim of reducing local recurrence, as well as reducing harmful side effects and cosmetic harm of traditional treatments. With it, there is a clinical need for real-time, non-invasive monitoring of subcutaneous heat deposition.

GPU accelerated 3D nonlinear time domain inversion of realistic breast phantoms with multiparameter optimization

Summary form only given. The detection of early-stage breast tumors with microwave imagers has received considerable attention in the recent years. However, reconstructing the complex dielectric profile of the realistic breast phantom remains a computationally costly challenge. This paper presents a GPU accelerated 3D time-domain nonlinear inverse scattering technique to effectively reconstruct the complex dielectric profile of realistic breast phantoms. The 3D nonlinear time domain inversion technique is based on the Born iterative method (BIM). BIM assumes that in the first iteration, the total field inside the object can be approximated by the incident field. Within each iteration of the BIM, both forward problem and inverse problem are solved once. Here the calculation of both the forward problem and the inverse problem are accelerated by the Tesla C2075 GPU from Nvidia. The acceleration method is based on the Compute Unified Device Architecture (CUDA) introduced by Nvidia to leverage the parallel computation power of its general-purpose GPU. In our method, the forward problem is solved with the Auxiliary Differential Equation Finite Difference Time Domain method (ADE FDTD) with the convolution perfectly matched layer (CPML). The main ADE FDTD algorithm to update the E and H fields, and the algorithm to update six CPML boundaries at the six sides of the domain are accelerated by different GPU kernels. Within each kernel, all the field points are calculated in parallel. However, each kernel is launched sequentially to avoid data race because different kernels may update the same field in the same region considering the overlap of PML slabs. The inversion is carried out with a regularized local optimization process, wherein a multi-parameter optimization scheme is designed to accommodate the three sets of unknowns, namely the real part of permittivity, conductivity, and a dispersion parameter. This process is also accelerated with the GPU while formulating the inversion matrix and solving the matrix with the conjugate gradient method. The acceleration has achieved a speedup factor of at least 25 for solving the forward problem and a speedup factor of 5 for the inversion while reconstructing the realistic breast phantom at 2mm voxel size. The realistic Wisconsin breast phantoms derived from MRI data are used here. The phantom provides a single-pole Debye relaxation model based complex dielectric profile of the breast tissue over our frequency of interest 0.5 to 3.7GHz. Imaging results for several phantoms will be shown and will demonstrate the reconstructed spatial distribution of the fiber glandular tissue of the breast. The comparison of the total computation expense between utilizing GPU and CPU will also be presented.