Dallan Byrne

Data Independent Radar Beamforming Algorithms for Breast Cancer Detection

Ultra wideband (UWB) Microwave imaging is one of the most promising emerging imaging technologies for breast cancer detection, and is based on the dielectric contrast between normal and cancerous tissues at microwave frequencies. UWB radar imaging involves illuminating the breast with a microwave pulse and re∞ected signals are used to determine the presence and location of signiflcant dielectric scatterers, which may be representative of cancerous tissue within the breast. Beamformers are used to spatially focus the re∞ected signals and to compensate for path dependent attenuation and phase efiects. While these beamforming algorithms have often been evaluated in isolation, variations in experimental conditions and metrics prompts the assessment of the beamformers on common anatomically and dielectrically representative breast models in order to efiectively compare the performance of each. This paper seeks to investigate the following beamforming algorithms: Monostatic and Multistatic Delay- And-Sum (DAS), Delay-Multiply-And-Sum (DMAS) and Improved Delay-And-Sum (IDAS). The performance of each beamformer is evaluated across a range of appropriate metrics.

Time-Domain Wideband Adaptive Beamforming for Radar Breast Imaging

A novel wideband microwave radar imaging method is presented to detect regions of significant dielectric contrast within the breast. Clutter reduction is paramount to any radar imaging algorithm, especially with clinical patient data where the tissue composition of the breast is inhomogeneous. Time-domain data-adaptive imaging methods have been previously applied in a narrowband manner for microwave radar breast imaging when the received signal spectral content was wideband. In this study, a wideband time-domain adaptive imaging approach is presented to perform data-adaptive focusing across the spectrum to reduce clutter. An equalization filter is adapted to compensate for the propagation distortion through tissue using a calculated estimate of the average dielectric properties of the breast. The effectiveness of the proposed wideband adaptive imaging approach is evaluated in conjunction with the delay-and-sum (DAS) method using numerical, experimental, and clinical data. Target scatterers are clearly detected while clutter levels are reduced significantly, between 4 and 6 dB, when compared to the DAS technique.

FDTD modeling of the breast: A review

Microwave imaging is one of the most promising emerging imaging technologies for breast cancer detection. Microwave imaging exploits the dielectric contrast between normal and malignant breast tissue at microwave frequencies. Many UWB radar imaging techniques require the development of accurate numerical phantoms to model the propagation and scattering of microwave signals within the breast. The Finite Difierence Time Domain (FDTD) method is the most commonly used numerical modeling technique used to model the propagation of Electromagnetic (EM) waves in biological tissue. However, it is critical that an FDTD model accurately represents the dielectric properties of the constituent tissues and the highly correlated distribution of these tissues within the breast. This paper presents a comprehensive review of the dielectric properties of normal and cancerous breast tissue, and the heterogeneity of normal breast tissue. Furthermore, existing FDTD models of the breast are examined and compared. This paper provides a basis for the development of more geometrically and dielectrically accurate numerical breast phantoms used in the development of robust microwave imaging algorithms.

Breast Cancer Detection Based on Differential Ultrawideband Microwave Radar

Ultrawideband (UWB) microwave imaging is a promising emerging method for the detection of breast cancer. Fibroglandular tissue has been shown to signiflcantly limit the efiectiveness of UWB imaging algorithms, particularly in the case of premenopausal women who may present with more dense breast tissue. Rather than trying to create an image of the breast, this study proposes to compare the UWB backscattered signals from successive scans of a dielectrically heterogeneous breast, to identify the presence of cancerous tissue. The temporal changes between signals are processed using Support Vector Machines to determine if a cancerous growth has occurred during the time between scans. Detection rates are compared to the results from a previous study by the authors, where UWB backscatter signals from a single scan were processed for cancer detection.

Transmitter-Grouping Robust Capon Beamforming for Breast Cancer Detection

Early detection of tumor tissue is one of the most signiflcant factors in the successful treatment of breast cancer. Microwave breast imaging methods are based on the dielectric contrast between normal and cancerous tissues at microwave frequencies. When the breast is illuminated with a microwave pulse, the dielectric contrast between these tissues can result in re∞ected backscatter. These re∞ected signals, containing tumor backscatter, are spatially focused using a beamformer which compensates for attenuation and phase efiects as the signal propagates through the breast. The beamformer generates an energy proflle of the breast where high energy regions suggest the presence of breast cancer. Data-Adaptive (DA) beamformers, use an approximation of the desired channel response based on the recorded signal data, as opposed to Data-Independent (DI) algorithms which use an assumed channel model. A novel extension of the DA Robust Capon Beamformer (RCB) is presented in this paper which is shown to signiflcantly outperform existing beamformers, particularly in a dielectrically heterogeneous breast. The algorithm is evaluated on three anatomically accurate electromagnetic (EM) breast models with varying amounts of heterogeneity. The novel beamforming algorithm is compared, using a range of performance metrics, against a number of existing beamformers.

Microwave radar imaging of heterogeneous breast tissue integrating a priori information

Conventional radar-based image reconstruction techniques fail when they are applied to heterogeneous breast tissue, since the underlying in-breast relative permittivity is unknown or assumed to be constant. This results in a systematic error during the process of image formation. A recent trend in microwave biomedical imaging is to extract the relative permittivity from the object under test to improve the image reconstruction quality and thereby to enhance the diagnostic assessment. In this paper, we present a novel radar-based methodology for microwave breast cancer detection in heterogeneous breast tissue integrating a 3D map of relative permittivity as a priori information. This leads to a novel image reconstruction formulation where the delay-and-sum focusing takes place in time rather than range domain. Results are shown for a heterogeneous dense (class-4) and a scattered fibroglandular (class-2) numerical breast phantom using Bristols 31-element array configuration.

Support Vector Machine-Based Ultrawideband Breast Cancer Detection System

Ultrawideband (UWB) microwave radar is a promising alternative breast screening method. Previous research has focused on the imaging and classification of early-stage breast cancer from backscattered microwave signals. The heterogeneous composition of breast tissue, prevalent among younger females, inhibits UWB scanning technologies to effectively localize cancerous regions within the breast. Rather than using UWB radar to image the breast or classify between types of cancer, the method proposed in this paper is to simply detect the presence of a tumor using a Support Vector Machine (SVM) based UWB cancer detection system. The SVM cancer detection system is evaluated using dielectrically realistic numerical breast models, and the performance of the detection system is compared to a Linear Discriminant Analysis (LDA) classification method.

Compound Radar Approach for Breast Imaging

Multistatic radar apertures record scattering at a number of receivers when the target is illuminated by a single transmitter, providing more scattering information than its monostatic counterpart per transmission angle. This paper considers the well-known problem of detecting tumor targets within breast phantoms using multistatic radar. To accurately image potentially cancerous targets size within the breast, a significant number of multistatic channels are required in order to adequately calibrate-out unwanted skin reflections, increase the immunity to clutter, and increase the dynamic range of a breast radar imaging system. However, increasing the density of antennas within a physical array is inevitably limited by the geometry of the antenna elements designed to operate with biological tissues at microwave frequencies. A novel compound imaging approach is presented to overcome these physical constraints and improve the imaging capabilities of a multistatic radar imaging modality for breast scanning applications. The number of transmit-receive (TX-RX) paths available for imaging are increased by performing a number of breast scans with varying array positions. A skin calibration method is presented to reduce the influence of skin reflections from each channel. Calibrated signals are applied to receive a beamforming method, compounding the data from each scan to produce a microwave radar breast profile. The proposed imaging method is evaluated with experimental data obtained from constructed phantoms of varying complexity, skin contour asymmetries, and challenging tumor positions and sizes. For each imaging scenario outlined in this study, the proposed compound imaging technique improves skin calibration, clearly detects small targets, and substantially reduces the level of undesirable clutter within the profile.

Less Becomes More for Microwave Imaging: Design and Validation of an Ultrawide-Band Measurement Array.

A compact, enclosed, ultrawide-band (UWB) antenna array is presented to acquire data for a quantitative microwave imaging method. Compared to existing systems, the proposed array allows a UWB antenna to be placed close to a target object while, at the same time, minimizing the volume of the imaging array. The antennas and metallic enclosure are designed to easily integrate with an iterative threedimensional (3-D) nonlinear inverse scattering technique. The volume of the internal imaging domain has been minimized for this particular architecture to reduce the computational time spent on reconstructing the dielectrics within the domain. Each cavity-backed element radiates toward the target and presents stable transmission characteristics across the 1-4-GHz band.

Prototype Ultra Wideband Radar System for Bladder Monitoring Applications

The aim of this study is to address the management of urinary problems by detecting changes in the volume of urine within the human bladder using low cost, low power, wearable Ultra Wideband (UWB) sensors and signal processing techniques. The paper describes experiments on the classiflcation of six three-layer dielectrically representative bladder phantoms, mimicking a range of muscle and bladder wall-to-wall distances. The process involves the illumination of the bladder with a UWB pulse. Due to the dielectric contrast between urine and bladder wall tissue at microwave frequencies, an electromagnetic re∞ection is generated at both the anterior and posterior bladder wall. These re∞ections are recorded, the salient features are extracted and processed by a classiflcation algorithm to estimate the volume of urine present in the bladder. To evaluate the prototype system, a number of physical bladder phantoms were constructed, each mimicking bladders of difierent volumes. Principal Component Analysis (PCA) was applied and the processed features were classifled by a K-Nearest Neighbour learning algorithm to estimate the state of the bladder (small, medium or full). The paper describes the bladder phantom prototype systems and the experimental setup. Results illustrate detection of phantom bladder states with an accuracy of up to 91%.