Panagiotis Kosmas

Time reversal with the FDTD method for microwave breast cancer detection

The feasibility of microwave breast cancer detection with a time-reversal (TR) algorithm is examined. This algorithm is based on the finite-difference time-domain method, and compensates for the wave decay and, therefore, is suitable for lossy media. In this paper, we consider a two-dimensional breast model based on magnetic resonance imaging data, and examine the focusing abilities of a TR mirror comprised of an array of receivers with a single ultra-wideband pulse excitation. In order to resolve small 3-mm-diameter tumors, a very short duration pulse is necessary, and this requirement may restrict the applicability of the system due to hardware limitations. We propose a way to overcome this obstacle based on the observation that the amplitude and phase information of the tumor response is sufficient to achieve focusing. The robustness of the TR algorithm with respect to breast inhomogeneities is demonstrated, and the good performance of the method suggests it is a promising technique for microwave breast cancer detection.

Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique.

PURPOSE Breast density measurement has the potential to play an important role in individualized breast cancer risk assessment and prevention decisions. Routine evaluation of breast density will require the availability of a low-cost, nonionizing, three-dimensional (3-D) tomographic imaging modality that exploits a strong properties contrast between dense fibroglandular tissue and less dense adipose tissue. The purpose of this computational study is to investigate the performance of 3-D tomography using low-power microwaves to reconstruct the spatial distribution of breast tissue dielectric properties and to evaluate the modality for application to breast density characterization. METHODS State-of-the-art 3-D numerical breast phantoms that are realistic in both structural and dielectric properties are employed. The test phantoms include one sample from each of four classes of mammographic breast density. Since the properties of these phantoms are known exactly, these testbeds serve as a rigorous benchmark for the imaging results. The distorted Born iterative imaging method is applied to simulated array measurements of the numerical phantoms. The forward solver in the imaging algorithm employs the finite-difference time-domain method of solving the time-domain Maxwells equations, and the dielectric profiles are estimated using an integral equation form of the Helmholtz wave equation. A multiple-frequency, bound-constrained, vector field inverse scattering solution is implemented that enables practical inversion of the large-scale 3-D problem. Knowledge of the frequency-dependent characteristic of breast tissues at microwave frequencies is exploited to obtain a parametric reconstruction of the dispersive dielectric profile of the interior of the breast. Imaging is performed on a high-resolution voxel basis and the solution is bounded by a known range of dielectric properties of the constituent breast tissues. The imaging method is validated using a breast phantom with a single, high-contrast interior scattering target in an otherwise homogeneous interior. The method is then used to image a set of realistic numerical breast phantoms of varied fibroglandular tissue density. RESULTS Imaging results are presented for each numerical phantom and show robustness of the method relative to tissue density. In each case, the distribution of fibroglandular tissues is well represented in the resulting images. The resolution of the images at the frequencies employed is wider than the feature dimensions of the normal tissue structures, resulting in a smearing of their reconstruction. CONCLUSIONS The results of this study support the utility of 3-D microwave tomography for imaging the distribution of normal tissues in the breast, specifically, dense fibroglandular tissue versus less dense adipose tissue, and suggest that further investigation of its use for volumetric evaluation of breast density is warranted.

Contrast-enhanced microwave imaging of breast tumors: a computational study using 3D realistic numerical phantoms

The detection of early-stage tumors in the breast by microwave imaging is challenged by both the moderate endogenous dielectric contrast between healthy and malignant glandular tissues and the spatial resolution available from illumination at microwave frequencies. The high endogenous dielectric contrast between adipose and fibroglandular tissue structures increases the difficulty of tumor detection due to the high dynamic range of the contrast function to be imaged and the low level of signal scattered from a tumor relative to the clutter scattered by normal tissue structures. Microwave inverse scattering techniques, used to estimate the complete spatial profile of the dielectric properties within the breast, have the potential to reconstruct both normal and cancerous tissue structures. However, the ill-posedness of the associated inverse problem often limits the frequency of microwave illumination to the UHF band within which early-stage cancers have sub-wavelength dimensions. In this computational study, we examine the reconstruction of small, compact tumors in three-dimensional numerical breast phantoms by a multiple-frequency inverse scattering solution. Computer models are also employed to investigate the use of exogenous contrast agents for enhancing tumor detection. Simulated array measurements are acquired before and after the introduction of the assumed contrast effects for two specific agents currently under consideration for breast imaging: microbubbles and carbon nanotubes. Differential images of the applied contrast demonstrate the potential of the approach for detecting the preferential uptake of contrast agents by malignant tissues.

FDTD-based time reversal for microwave breast cancer Detection-localization in three dimensions

In a previous study, a novel time-reversal (TR) algorithm based on the finite-difference time-domain (FDTD) method for ultra-wideband microwave breast cancer detection was presented. The system properties and performance were originally studied for two-dimensional (2-D) simplified models and geometries and, more recently, for a realistic breast model based on magnetic resonance imaging measured data. This paper extends this FDTD TR algorithm to a three-dimensional (3-D) case in order to study localization of the tumor target in 3-D. The FDTD TR algorithm solves the full 3-D wave equation and, thus, takes into account polarization effects. In order to compare the new images with previous results, we consider a 2-D planar receiver array, which is an extension of the line of receivers introduced in the 2-D model. A direct comparison from 3-D to 2-D reconstructions illustrates the advantages of using the fully 3-D algorithm, which counterbalances its additional computational cost.

A matched-filter FDTD-based time reversal approach for microwave breast cancer detection

Based on the finite-difference time-domain (FDTD) method, a numerical time-reversal (TR) algorithm for microwave breast cancer detection, already presented in previous work , , is further examined. In , we assumed that the exact field scattered from the tumor-like anomaly is available for backpropagation, and it was shown that the time reversal process is robust to breast inhomogeneities and uncertainties of the skin thickness or electric properties. In this paper, we use the same time reversal mirror (TRM) and two-dimensional (2-D) breast model based on magnetic resonance imaging (MRI) data, but examine the realistic situation where the target response is not known and can only be estimated from the total signal, which is dominated by clutter. A matched-filter approach to solve this signal processing problem is proposed and applied to the TRM data. Detection and localization is achieved for different target locations, and the ability of the time reversal algorithm to avoid false alarms is demonstrated.

Modeling with the FDTD method for microwave breast cancer detection

This paper addresses important issues related to finite-difference time-domain modeling for microwave breast cancer detection. We present a simple and efficient way of modeling dispersion for various types of biological tissue, in the range of 30 MHz-20 GHz. Propagation and absorbing boundary conditions are modified accordingly. Results from three-dimensional simulations of a semiellipsoid geometric representation of the breast terminated by a planar chest wall illustrate the effect of certain important aspects of the detection problem including: 1) the pulse distorting effects of propagation in frequency-dependent tissue; 2) the choice of the surrounding medium; and 3) the transmitter location relative to the breast and chest wall. In particular, it is shown that the presence of the chest wall can affect greatly the systems detection abilities, even for tumors that are not located in the proximity of the chest wall.

Feasibility Study of Lesion Classification via Contrast-Agent-Aided UWB Breast Imaging

This letter investigates the feasibility of applying contrast agents for lesion classification in ultra wideband (UWB) breast imaging. Previous study has focused on distinguishing benign from malignant masses by exploiting their morphology-dependent backscatter signature via the complex natural resonances of the late-time target response. The tissue differentiation capability, however, deteriorates severely if the intrinsic contrast between the dielectric properties of dysplastic and normal tissues are small. A possible solution to this problem is proposed in this letter via the use of microwave contrast agents, where the damping factors of the differential backscatter responses before and after the infusion of contrast agents to a dysplastic inclusion are used to correlate with the anomaly shapes. The feasibility of this approach for lesion classification is demonstrated through comprehensive simulation studies using realistic numerical breast models.

Detection and Localization of Tissue Malignancy Using Contrast-Enhanced Microwave Imaging: Exploring Information Theoretic Criteria

We present a new approach to the problem of detecting cancerous tissues at low-to-medium signal-to-noise ratios (SNRs) in an interference-prone biological medium, where the dielectric properties of the surrounding heterogeneous healthy tissues are comparable to those of the tumors. Suppose that microwave contrast agents, such as microbubbles or nanocomposites, are selectively delivered to the cancer site via systemic administration, and the difference between the backscatter responses (differential signal) before and after the administration of contrast medium to the tissue anomalies can be extracted. We can then formulate the problem from the perspective of signal model selection. Subsequently, two information theoretic criteria (ITC), namely the Akaike information criterion (AIC) and the minimum description length (MDL), are applied as a blind method to reliably detect the malignant tumor and estimate its location using ITC-oriented strategies. Finally, numerical examples based on a 2-D canonical biological phantom, which synthesizes an interference-prone microwave imaging scenario, are carried out to evaluate the performance of the proposed ITC-based algorithms. The dielectric properties of the phantom are varied to investigate diagnostics of three types of dysplastic tissues: liver, lung, and breast cancers. We also use a 3-D anatomically realistic breast model as a testbed to verify the effectiveness of the proposed method.

Multiple-Input Multiple-Output Radar for Lesion Classification in Ultrawideband Breast Imaging

This paper studies the problem of applying multiple-input multiple-output (MIMO) radar techniques for lesion classification in ultrawideband (UWB) breast imaging. Ongoing work on this topic has suggested that benign and malignant masses, which usually possess remarkable architectural differences, could be distinguished by exploiting their morphology-dependent UWB microwave backscatter. We have previously approached this problem by deriving the complex natural resonances of the late-time target response, where the damping factors vary with the border profiles of anomalies. In this paper, we investigate the potential advantage of MIMO radar to enhance the resonance scattering phenomenon in breast tissue discrimination. MIMO radar can choose freely the probing signals transmitted via its antennas to exploit the independence between signals at the array elements, thereby enhancing the performance of target classification. Based on the observed damping factors and the receiver operating characteristics at different classifiers, which correspond to various diversity paths in the MIMO radar system, two data-fusion rules are proposed for robust lesion differentiation. Finally, numerical examples are provided to demonstrate the efficacy of the proposed imaging technique.

Periodic FDTD Analysis of a 2-D Leaky-Wave Planar Antenna Based on Dipole Frequency Selective Surfaces

A periodic finite-difference time-domain (FDTD) analysis is presented and applied for the first time in the study of a two-dimensional (2D) leaky-wave planar antenna based on dipole frequency selective surfaces (FSSs). First, the effect of certain aspects of the FDTD modeling in the modal analysis of complex waves is studied in detail. Then, the FDTD model is used for the dispersion analysis of the antenna of interest. The calculated values of the leaky-wave attenuation constants suggest that, for an antenna of this type and moderate length, a significant amount of power reaches the edges of the antenna, and thus diffraction can play an important role. To test the validity of our dispersion analysis, measured radiation patterns of a fabricated prototype are presented and compared with those predicted by a leaky-wave approach based on the periodic FDTD results.