Microwave Image Reconstruction of Tissue Property Dispersion Characteristics Utilizing Multiple-Frequency Information

Abstract

A multiple-frequency-dispersion reconstruction algorithm utilizing a Gauss-Newton iterative strategy is presented for microwave imaging. This algorithm facilitates the simultaneous use of multiple-frequency measurement data in a single image reconstruction. Using the stabilizing effects of the low-frequency measurement data, higher frequency data can be included to reconstruct images with improved resolution. The parameters reconstructed in this implementation are now frequency-independent dispersion coefficients instead of the actual properties and may provide new diagnostic information. In this paper, large high-contrast objects are successfully constructed utilizing assumed simple dispersion models for both simulation and phantom cases for which the traditional single-frequency algorithm previously failed. Consistent improvement in image quality can be observed by involving more frequencies in the reconstruction; however, there appears to be a limit to how closely spaced the frequencies can be chosen while still providing independent new information. Possibilities for fine-tuning the image reconstruction performance in this context include: 1) variations of the assumed dispersion model and 2) Jacobian matrix column and row weighting schemes. Techniques for further reducing the forward solution computation time using time-domain solvers are also briefly discussed. The proposed dispersion reconstruction technique is quite general and can also be utilized in conjunction with other Gauss-Newton-based algorithms including the log-magnitude phase-form algorithm.Index Terms-Column weighting, finite-difference time-domain (FDTD) method, microwave imaging, multiple frequency-dispersion reconstruction (MFDR), row weighting.

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

“…Experimental phantoms may be constructed from synthetic materials that accurately mimic tissue dielectric properties, 32 but the diverse and complex structural distributions of the breast are difficult to mimic in an experimental phantom. Accordingly, many prior experimental laboratory studies have been restricted to models consisting of arrangements of homogeneous cylindrical 24,26,33 or spherical 17,18,34 targets which do not accurately mimic the complexity of the breast. Numerical phantoms offer more flexibility in capturing the structural realism of breast tissue.…”

“…[21][22][23][24][25][26][27] Namely, the testbeds employed in this work are state-of-the-art 3-D numerical breast phantoms 13 that are realistic in both dielectric properties and tissue configurations, and for which the known actual properties distributions serve as a rigorous benchmark for the imaging results. Recent large-scale dielectric spectroscopy studies of freshly excised healthy and diseased breast tissue 2,14 suggest that much of the prior microwave breast imaging research assumed too large of a dielectric contrast between cancerous and healthy glandular tissues and too small of a contrast between normal glandular tissue and fat.…”

3D Microwave Breast Imaging Based on Multistatic Radar Concept System

“…Two different active MI methods are available for breast cancer detection: microwave tomography (MT) [2]～ [6] and UWB radar techniques [7]～ [9]. The goal of the UWB MI radar systems is to reconstruct the image of scatterer distribution only.…”

“…One of the first systems for early breast cancer detection was the MT system developed in Dartmouth College, USA [2], [3], [6], [13]～ [15]. This system is currently being investigated in many clinical trials with real patients.…”

“…-Increasing maximal operating frequency, using multi-frequency and UWB methods of reconstruction [6], [9], [12], [19], [21], [22]; -Acquisition of near-field components of signals from scattering objects [22].…”