Martin O'Halloran

Quasi-Multistatic MIST Beamforming for the Early Detection of Breast Cancer

Microwave imaging via space-time (MIST) beamforming has been shown to be one of the most promising imaging modalities for detecting small malignant breast tumors. This paper outlines two modifications to the MIST system developed by Hagness for the early detection of breast cancer, resulting in a quasi-multistatic MIST beamformer (multi-MIST). Multistatic MIST beamforming involves illuminating the breast with an ultrawideband (UWB) signal from one antenna while collecting the reflections at an array of antennas, as opposed to traditional monostatic MIST beamforming where only the transmitting antenna records the reflections from the breast. In order to process the multistatic data, traditional data-adaptive artifact removal algorithms have to be modified to accommodate signals from all antennas. Also, the MIST beamforming algorithm, which spatially focuses the signal and compensates for frequency-dependent propagation effects, has to be modified. The algorithms are tested on a 2-D anatomically accurate finite-difference time-domain model of the breast. The multi-MIST beamformer described here is shown to offer an improved signal to clutter ratio when compared to the traditional monostatic MIST beamformer.

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.

SUPPORT VECTOR MACHINES FOR THE CLASSIFICATION OF EARLY-STAGE BREAST CANCER BASED ON RADAR TARGET SIGNATURES

Microwave Imaging (MI) has been widely investigated as a method to detect early stage breast cancer based on the dielectric contrast between normal and cancerous breast tissue at microwave frequencies. Furthermore, classiflcation methods have been developed to difierentiate between malignant and benign tumours. To successfully classify tumours using Ultra Wideband (UWB) radar, other features have to be examined other than simply the dielectric contrast between benign and malignant tumours, as contrast alone has been shown to be insuflcient. In this context, previous studies have investigated the use of the Radar Target Signature (RTS) of tumours to give valuable information about the size, shape and surface texture. In this study, a novel classiflcation method is examined, using Principal Component Analysis (PCA) to extract the most important tumour features from the RTS. Support Vector Machines (SVM) are then applied to the principal components as a method of classifying these tumours. Finally, several difierent classiflcation architectures are compared. In this study, the performance of classiflers is tested using a database of 352 tumour models, comprising four difierent sizes and shapes, using the cross validation method.

COMPARISON OF PLANAR AND CIRCULAR ANTENNA CONFIGURATIONS FOR BREAST CANCER DETECTION USING MICROWAVE IMAGING

Ultra Wideband (UWB) radar is a promising emerging technology for breast cancer detection based on the dielectric contrast between normal and tumor tissues at microwave frequencies. One of the most important considerations in developing a UWB imaging system is the conflguration of the antenna array. Two speciflc conflgurations are currently under investigation, planar and circular. The planar conflguration involves placing a conformal array of antennas on the naturally ∞attened breast with the patient lying in the supine position. Conversely, the circular conflguration involves the patient lying in the prone position, with the breast surrounded by a circular array of antennas. In order to efiectively test the two antenna conflgurations, two 2D Finite-Difierence Time-Domain (FDTD) models of the breast are created, and are used to simulate backscattered signals generated when the breast is illuminated by UWB pulses. The backscattered signals recorded from each antenna conflguration are passed through a UWB beamformer and images of the backscattered energy are created. The performance of each imaging approach is evaluated by both quantitative methods and visual inspection, for a number of test conditions. System performance as a function of number of antennas, variation in tissue properties, and tumor location are examined.

Investigation of Classifiers for Early-Stage Breast Cancer Based on Radar Target Signatures

Ultra Wideband (UWB) radar has been extensively investigated as a means of detecting early-stage breast cancer. The basis for this imaging modality is the dielectric contrast between normal and cancerous breast tissue at microwave frequencies. However, based on the dielectric similarities between a malignant and a benign tumour within the breast, difierentiating between these types of tissues in microwave images may be problematic. Therefore, it is important to investigate alternative methods to analyse and classify dielectric scatterers within the breast, taking into account other tumour characteristics such as shape and surface texture of tumours. Benign tumours tend to have smooth surfaces and oval shapes whereas malignant tumours tend to have rough and complex surfaces with spicules or microlobules. Consequently, one classiflcation approach is to classify scatterers based on their Radar Target Signature (RTS), which carries important information about scatterer size and shape. In this paper, Gaussian Random Spheres (GRS) are used to model the shape and size of benign and malignant tumours. Principal Components Analysis (PCA) is used to extract information from the RTS of the tumours, while eight difierent combinations of tumour classiflers are analysed in terms of performance and are compared in terms of two possible approaches: Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).

ROTATING ANTENNA MICROWAVE IMAGING SYSTEM FOR BREAST CANCER DETECTION

Breast imaging using Confocal Microwave Imaging (CMI) has becoming a di-cult problem, primarily due to the recently- established dielectric heterogeneity of normal breast tissue. CMI for breast cancer detection was originally developed based on several assumptions regarding the dielectric properties of normal and cancerous breast tissue. Historical studies which examined the dielectric properties of breast tissue concluded that the breast was primarily dielectrically homogeneous, and that and that the propagation, attenuation and phase characteristics of normal breast tissue allowed for the constructive addition of the Ultra Wideband (UWB) returns from dielectric scatterers within the breast. However, recent studies by Lazebnik et al. have highlighted a very signiflcant dielectric contrast between normal adipose and flbroglandular tissue within the breast. Lazebnik also established that there was an almost negligible dielectric contrast between flbroglandular and cancerous breast tissue at microwave frequencies. This dielectric heterogeneity presents a considerably more challenging imaging scenario, where constructive addition of the UWB returns, and therefore tumor detection, is much more di-cult. Therefore, more sophisticated signal acquisition and beamforming algorithms need to be developed. In this paper, a novel imaging algorithm is described, which uses a rotating antenna system to increase the number of unique propagation paths to and from the tumor to create an improved image of the breast. This approach is shown to provide improved images of more dielectrically heterogeneous breasts than the traditional flxed-antenna delay and sum beamformer from which it is derived.

Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast

The considerable overlap in the dielectric properties of benign and malignant tissue at microwave frequencies means that breast tumour classiflcation using traditional UWB Radar imaging algorithms could be very problematic. Several studies have examined the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be in∞uenced by the size, shape and surface texture of tumours. The main weakness of existing studies is that they mainly consider tumours in a 3D dielectrically homogenous or 2D heterogeneous breast model. In this paper, the efiects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifler are examined in terms of both sensitivity and speciflcity, using a 3D dielectrically heterogeneous breast model. The performance of the SNN classifler is compared to an existing LDA classifler. The efiect of combining con∞icting classiflcation readings in a multi-antenna system is also considered. Finally and importantly, misclassifled tumours are analysed and suggestions for future work are discussed.

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.

Hybrid Artifact Removal for Confocal Microwave Breast Imaging

Several factors determine the effectiveness of an early-stage artifact removal algorithm for the detection of breast cancer using confocal microwave imaging. These factors include the ability to select the correct time window containing the artifact, the ability to remove the artifact while being robust to normal variances, and ability to effectively preserve the tumor response in the resultant signal. Very few (if any) of the existing artifact removal algorithms incorporate all of these qualities. In this letter, a novel hybrid artifact removal algorithm for microwave breast imaging applications is presented, which combines the best attributes of two existing algorithms to effectively remove the early-stage artifact while preserving the tumor response. This algorithm is compared to existing algorithms using a range of appropriate performance metrics.

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.