Douglas Kurrant

An Improved Technique to Predict the Time-of-Arrival of a Tumor Response in Radar-Based Breast Imaging

Radar-based microwave imaging has been proposed as a complementary modality for early-stage breast cancer screening. This paper presents an algorithm that may be used to accurately predict the time-of-arrival (TOA) of a tumor response contained in sample data acquired from a small number of antennas in a realistic scenario. The TOA information may be used by many of the existing radar-based methods to detect and localize a tumor response. The robustness of the algorithm is demonstrated with data generated from realistic numerical breast models.

Tumor Response Estimation in Radar-Based Microwave Breast Cancer Detection

Radar-based microwave imaging techniques have been proposed for early stage breast cancer detection. A considerable challenge for the successful implementation of these techniques is the reduction of clutter, or components of the signal originating from objects other than the tumor. In particular, the reduction of clutter from the late-time scattered fields is required in order to detect small (subcentimeter diameter) tumors. In this paper, a method to estimate the tumor response contained in the late-time scattered fields is presented. The method uses a parametric function to model the tumor response. A maximum a posteriori estimation approach is used to evaluate the optimal values for the estimates of the parameters. A pattern classification technique is then used to validate the estimation. The ability of the algorithm to estimate a tumor response is demonstrated by using both experimental and simulated data obtained with a tissue sensing adaptive radar system.

Breast Imaging Using Microwave Tomography with Radar-Based Tissue-Regions Estimation

Microwave tomography (MWT) and a radar-based region estimation technique are combined to create a novel algorithm for biomedical imaging with a focus on breast cancer detection and monitoring. The region estimation approach is used to generate a patient-specific spatial map of the breast anatomy that includes skin, adipose and fibroglandular regions, as well as their average dielectric properties. This map is incorporated as a numerical inhomogeneous background into an MWT algorithm based on the finite element contrast source inversion (FEM-CSI) method. The combined approach reconstructs finer structural details of the breast and better estimates the dielectric properties than either technique used separately. Numerical results obtained with the novel combined algorithmic approach, based on synthetically generated breast phantoms, show significant improvement in image quality.

Regional estimation of the dielectric properties of inhomogeneous objects using near-field reflection data

We present a new inversion strategy that integrates radar-based methods with microwave tomography (MT) to efficiently provide low resolution information about an objects structure and average dielectric properties. For this preliminary investigation, we assume that the object may be characterized as having three regions: a thin outer layer and an interior with two inhomogeneous regions having dissimilar average dielectric properties. Our aim is not to reconstruct a detailed image of an object, but rather to provide information about its basic structure, including the geometric and mean dielectric properties of regions predominantly composed of a given material. The inversion technique is carried out in two steps. First, a reconstruction model indicating the locations and spatial features of the three regions of interest is constructed efficiently and quickly using ultrawideband (UWB) reflection data. The reconstruction model formed using radar-based techniques is then incorporated into the second step of the procedure which estimates the mean dielectric properties over each region using MT methods. Identifying the three homogeneous regions with radar-based techniques provides a priori information about an objects internal geometry and significantly simplifies the parameter space structure so that the inverse scattering problem solved with MT is not as ill-posed as those typically encountered. The performance of the proposed technique is first evaluated with both reflection and transmission data generated by progressively more complex 2D numerical models. Microwave breast imaging approaches would benefit from the internal structural information extracted by the algorithm, so a practical application is explored using 2D breast models formed from the magnetic resonance (MR) scans of a patient study. The algorithms ability to infer the breasts basic internal structure is demonstrated with these examples.

Evaluation of 3-D Acquisition Surfaces for Radar-Based Microwave Breast Imaging

This study investigates the impact that the acquisition surface has on the internal coverage of an object in the context of radar-based near-field microwave (MW) breast imaging. We define an acquisition surface as the surface over, which data are collected. Three different three-dimensional (3-D) data acquisition surfaces are investigated: 1) cylindrical, 2) hemispherical, and 3) patient specific. Three 3-D numerical breast models are used for the study. A realistic ultra-wideband (UWB) antenna generates incident fields and records the total fields. The responses from targets are analyzed, and object coverage is evaluated in terms of range distances, cross-range distances, and cumulative radiated power directed into the object by the antenna array embedded in the acquisition surface. Images are formed to verify these observations. We demonstrate that a patient-specific acquisition surface provides greater responses from targets, superior object coverage and improved images compared to the other acquisition surfaces studied.

Technique to Decompose Near-Field Reflection Data Generated From an Object Consisting of Thin Dielectric Layers

Extracting properties of hidden structures using ultra-wideband (UWB) radar is evolving into a promising technology. For these applications, a short-duration electromagnetic wave is transmitted into an object or structure of interest and the backscattered fields that arise due to dielectric contrasts at interfaces are measured. The time-of-arrival (TOA) between reflections and the amplitude of the reflections may be used to infer the geometrical and dielectric properties of hidden structures or objects. For electrically thin layers, the limited bandwidth of the illuminating signal typically gives rise to overlapping reflections, necessitating the use of high-resolution techniques. We investigate an iterative nonlinear parameter estimation technique that may be used for near-field applications. The effectiveness of the algorithm to decompose the reflection data is evaluated using numerical data generated from 2D and 3D dielectric slabs and experimental data from multi-layered slabs.

Defining regions of interest for microwave imaging using near-field reflection data

Microwave imaging benefits from information on the internal structure of the object of interest. Previously, we developed a tool to define regions dominated by a particular material and tested this approach with 2-D simulations of breast models. In this paper, we apply the technique to experimental data with the goal to extract an objects internal structural information using radar-based techniques. Specifically, the technique extracts information from microwave reflection data in order to identify discontinuities in material properties. By combining observations from multiple antenna locations, this information is used to estimate regions of interest. The utility of the tool is demonstrated in practical scenarios where the data are generated experimentally from cylindrical models using an ultra-wideband sensor.

Tumor Estimation In Tissue Sensing Adaptive Radar (TSAR) Signals

Tissue sensing adaptive radar (TSAR) is a microwave imaging technique that has been proposed as a modality for early stage breast cancer detection. A considerable challenge for the successful implementation of this technique is the reduction of clutter that is present in the scattered fields. In this paper, a method to estimate the tumor response contained in the late time scattered fields is presented. The method uses a parametric function to model the tumor response. A maximum-a-posteriori (MAP) estimation approach is used to evaluate the optimal values for the estimates of the parameters. Accurate identification of the tumor response permits the segregation of the clutter from the late time response. The ability of the algorithm to estimate a tumor response using simulated TSAR data is demonstrated.

Extraction of Internal Spatial Features of Inhomogeneous Dielectric Objects Using Near-Field Reflection Data

Ultra-wideband (UWB) microwave radar imaging tech- niques provide a non-invasive means to extract information related to an objects internal structure. For these applications, a short-duration electromagnetic wave is transmitted into an object of interest and the backscattered flelds that arise due to dielectric contrasts at interfaces are measured. In this paper, we present a method that may be used to estimate the time-of-arrival (TOA) parameter associated with each re∞ection that arises due to a dielectric property discontinuity (or di- electric interface). A second method uses this information to identify the locations of points on these interfaces. When data are collected at a number of sensor locations surrounding the object, the collection of points may be used to estimate the shape of contours that segre- gate and enclose dissimilar regions within the object. The algorithm is tested with data generated when a cylindrical wave is applied to a num- ber of numerical 2D models of increasing complexity. Moreover, the algorithms feasibility is evaluated using data generated from breast models constructed from magnetic resonance (MR) breast scans. Re- sults show that this is a promising approach to identifying regions and the internal structure within the breast.

Integrating prior information into microwave tomography Part 1: Impact of detail on image quality

Purpose: The authors investigate the impact that incremental increases in the level of detail of patient‐specific prior information have on image quality and the convergence behavior of an inversion algorithm in the context of near‐field microwave breast imaging. A methodology is presented that uses image quality measures to characterize the ability of the algorithm to reconstruct both internal structures and lesions embedded in fibroglandular tissue. The approach permits key aspects that impact the quality of reconstruction of these structures to be identified and quantified. This provides insight into opportunities to improve image reconstruction performance. Methods: Patient‐specific information is acquired using radar‐based methods that form a regional map of the breast. This map is then incorporated into a microwave tomography algorithm. Previous investigations have demonstrated the effectiveness of this approach to improve image quality when applied to data generated with two‐dimensional (2D) numerical models. The present study extends this work by generating prior information that is customized to vary the degree of structural detail to facilitate the investigation of the role of prior information in image formation. Numerical 2D breast models constructed from magnetic resonance (MR) scans, and reconstructions formed with a three‐dimensional (3D) numerical breast model are used to assess if trends observed for the 2D results can be extended to 3D scenarios. Results: For the blind reconstruction scenario (i.e., no prior information), the breast surface is not accurately identified and internal structures are not clearly resolved. A substantial improvement in image quality is achieved by incorporating the skin surface map and constraining the imaging domain to the breast. Internal features within the breast appear in the reconstructed image. However, it is challenging to discriminate between adipose and glandular regions and there are inaccuracies in both the structural properties of the glandular region and the dielectric properties reconstructed within this structure. Using a regional map with a skin layer only marginally improves this situation. Increasing the structural detail in the prior information to include internal features leads to reconstructions for which the interface that delineates the fat and gland regions can be inferred. Different features within the glandular region corresponding to tissues with varying relative permittivity values, such as a lesion embedded within glandular structure, emerge in the reconstructed images. Conclusion: Including knowledge of the breast surface and skin layer leads to a substantial improvement in image quality compared to the blind case, but the images have limited diagnostic utility for applications such as tumor response tracking. The diagnostic utility of the reconstruction technique is improved considerably when patient‐specific structural information is used. This qualitative observation is supported quantitatively with image metrics.