Microwave breast imaging (using electromagnetic waves of frequencies around 1 GHz) has mostly remained at the research level for the past decade, gaining little clinical acceptance. The major hurdles limiting patient use are both at the hardware level (challenges in collecting accurate and noncorrupted data) and software level (often plagued by unrealistic reconstruction times in the tens of hours). In this paper we report improvements that address both issues. First, the hardware is able to measure signals down to levels compatible with sub-centimeter image resolution while keeping an exam time under 2 min. Second, the software overcomes the enormous time burden and produces similarly accurate images in less than 20 min. The combination of the new hardware and software allows us to produce and report here the first clinical 3-D microwave tomographic images of the breast. Two clinical examples are selected out of 400+ exams conducted at the Dartmouth Hitchcock Medical Center (Lebanon, NH). The first example demonstrates the potential usefulness of our system for breast cancer screening while the second example focuses on therapy monitoring.

Spatial unwrapping of the phase component of time varying electromagnetic fields has important implications in a range of disciplines including synthetic aperture radar (SAR) interferometry, MRI, optical confocal microscopy, and microwave tomography. This paper presents a fundamental framework based on the phase unwrapping integral, especially in the complex case where phase singularities are enclosed within the closed path integral. With respect to the phase unwrapping required when utilized in Gauss-Newton iterative microwave image reconstruction, the concept of dynamic phase unwrapping is introduced where the singularity location varies as a function of the iteratively modified property distributions. Strategies for dynamic phase unwrapping in the microwave problem were developed and successfully tested in simulations and clinical experiments utilizing large, high contrast targets to validate the approach.

An efficient Gauss-Newton iterative imaging technique utilizing a three-dimensional (3-D) field solution coupled to a two-dimensional (2-D) parameter estimation scheme (3-D/2-D) is presented for microwave tomographic imaging in medical applications. While electromagnetic wave propagation is described fully by a 3-D vector field, a 3-D scalar model has been applied to improve the efficiency of the iterative reconstruction process with apparently limited reduction in accuracy. In addition, the image recovery has been restricted to 2-D but is generalizable to three dimensions. Image artifacts related primarily to 3-D effects are reduced when compared with results from an entirely two-dimensional inversion (2-D/2-D). Important advances in terms of improving algorithmic efficiency include use of a block solver for computing the field solutions and application of the dual mesh scheme and adjoint approach for Jacobian construction. Methods which enhance the image quality such as the log-magnitude/unwrapped phase minimization were also applied. Results obtained from synthetic measurement data show that the new 3-D/2-D algorithm consistently outperforms its 2-D/2-D counterpart in terms of reducing the effective imaging slice thickness in both permittivity and conductivity images over a range of inclusion sizes and background medium contrasts.

Reconstructing images of large high-contrast objects with microwave methods has proved difficult. Successful images have generally been obtained by using a priori information to constrain the image reconstruction to recover the correct electromagnetic property distribution. In these situations, the measured electric field phases as a function of receiver position around the periphery of the imaging field-of-view vary rapidly often undergoing changes of greater than pi radians especially when the object contrast and illumination frequency increase. In this paper, we introduce a modified form of a Maxwell equation model-based image reconstruction algorithm which directly incorporates log-magnitude and phase of the measured electric field data. By doing so, measured phase variation can be unwrapped and distributed over more than one Rieman sheet in the complex plane. Simulation studies and microwave imaging experiments demonstrate that significant image quality enhancements occur with this approach for large high-contrast objects. Simple strategies for visualizing and unwrapping phase values as a function of the transmitter and receiver positions within our microwave imaging array are described. Metrics of the degree of phase variation expressed in terms of the amount and extent of phase wrapping are defined and found to be figures-of-merit which estimate when it is critical to deploy the new image reconstruction approach. In these cases, the new algorithm recovers high-quality images without resorting to the use of a priori information on object contrast and/or size as previously required.

Model-based imaging techniques utilizing microwave signal illumination rely heavily on the ability to accurately represent the wave propagation with a suitable numerical model. To date, the highest quality images from our prototype system have been achieved utilizing a single transmitter/single receiver measurement system where both antennas are manually repositioned to facilitate multiple illuminations of the imaging region, thus requiring long data acquisition times. In an effort to develop a system that can acquire data in a real time manner, a 32-channel network has been fabricated with all ports capable of being electronically selected for either transmit or receive mode. The presence of a complete array of antenna elements at data collection time perturbs the field distributions being measured, which can subsequently degrade the image reconstruction due to increased data-model mismatch. Incorporating the nonactive antenna-compensation model from Part I of this paper into our hybrid element near field image reconstruction algorithm is shown to restore image quality when fixed antenna-array data acquisition is used. Improvements are most dramatic for inclusions located in near proximity to the antenna array itself, although cases of improvement in the recovery of centered heterogeneities are also illustrated. Increases in the frequency of illumination are found to warrant an increased need for nonactive antenna compensation. Quantitative measures of recovered inclusion shape and position reveal a systematic improvement in image reconstruction quality when the nonactive antenna-compensation model is employed. Improvements in electrical property value recovery of localized heterogeneities are also observed. Image reconstructions in freshly excised breast tissue illustrate the applicability of the approach when used with our two-dimensional microwave imaging system.

Fixed-array microwave imaging with multisensor data acquisition can suffer from nonactive antenna element interactions which cause distortions in the measurements. In Part I of a two-part paper, we develop a nonactive antenna compensation model for incorporation in model-based near-field microwave image reconstruction methods. The model treats the nonactive members of the antenna array as impedance boundary conditions applied over a cylindrical surface of finite radius providing two parameters, the effective antenna radius and impedance factor, which can be determined empirically from measured data. Results show that the effective radius and impedance factor provide improved fits to experimental data in homogeneous phantoms where measurements are obtained with and without the presence of the nonactive antenna elements. Once deduced, these parameters are incorporated into the nonactive antenna compensation model and lead to systematic data-model match improvements in heterogeneous phantoms. While the improvements afforded by the nonactive antenna model are small on a per measurement basis, they are not insignificant. As shown in Part II, inclusion of this new model for nonactive antenna compensation produces significantly higher quality image reconstructions from measurements obtained with a fixed-array data acquisition system over the frequency band 500-900 MHz.

The finite element (FE) method has found several applications in emerging imaging modalities, especially microwave imaging which has been shown to be potentially useful in a number of areas including thermal estimation. In monitoring temperature distributions, the biological phenomena of temperature variations of tissue dielectric properties is exploited. By imaging these properties and their changes during such therapies as hyperthermia, temperature distributions can be deduced using difference imaging techniques. The authors focus on a microwave imaging problem where the hybrid element (HE) method is used in conjunction with a dual mesh scheme in an effort to image complex wavenumbers, k(2). The dual mesh scheme is introduced to improve the reconstructed images of tissue properties and is ideally suited for systems using FE methods as their computational base. Since the electric fields typically vary rapidly over a given body when irradiated by high-frequency electromagnetic sources, a dense mesh is needed for these fields to be accurately represented. Conversely, k(2) may be fairly constant over subregions of the body which would allow for a less dense sampling of this parameter in those regions. In the dual mesh system employed, the first mesh, which is uniformly dense, is used for calculating the electric fields over the body whereas the second mesh, which is nonuniform and less dense, is used for representing the k(2) distribution within the region of interest. The authors examine the 2-D TM polarization case for a pair of dielectric distributions on both a large and small problem to demonstrate the flexibility of the dual mesh method along with some of the difficulties associated with larger imaging problems. Results demonstrate the capabilities of the dual mesh concept in comparison to a single mesh approach for a variety of test cases, suggesting that the dual mesh method is critical for FE based image reconstruction where rapidly varying physical quantities are used to recover smoother property profiles, as can occur in microwave imaging of biological bodies.