Richard Obermeier

Fusing digital-breast-tomosynthesis and nearfield-radar-imaging information for a breast cancer detection algorithm

This paper presents a computational simulation of a hybrid Digital-Breast-Tomosynthesis (DBT) and microwave Nearfield Radar Imaging (NRI) technique for detecting breast cancer. The DBT image is used to obtain a background distribution of the heterogeneous tissues in the breast, which is then used by the NRI algorithm to successfully detect cancerous tissues in the breast, in spite of the low dielectric contrast between cancerous and normal glandular/fibro-connective tissues.

3D Microwave Nearfield Radar Imaging (NRI) using digital breast tomosynthesis (DBT) for non-invasive breast cancer detection

Describes our latest results in the design of new 3D breast cancer imaging system that combines Microwave Nearfield Radar Imaging (NRI) and Digital Breast Tomosynthesis (DBT).

Imaging breast cancer in a hybrid DBT / NRI system using compressive sensing

This work presents an improved imaging algorithm for use in a hybrid Digital Breast Tomosynthesis (DBT) / microwave Nearfield Radar Imaging (NRI) system. By using compressive sensing techniques, this new algorithm is capable of reconstructing the dielectric constant and conductivity of breast cancer with a small number of measurements.

A compressive sensing approach for enhancing breast cancer detection using a hybrid DBT/NRI configuration

This work presents a novel breast cancer imaging approach that uses compressive sensing in a hybrid digital breast tomosynthesis (DBT)/nearfield radar imaging (NRI) system configuration. The non-homogeneous tissue distribution of the breast, described in terms of dielectric constant and conductivity, is extracted from the DBT image, and it is used by a full-wave finite difference in the frequency domain method to build a linearized model of the non-linear NRI imaging problem. The inversion of the linear problem is solved using compressive sensing imaging techniques, which lead to a reduction on the required number of sensing antennas and operational bandwidth without loss of performance.

Model-based Optimization of Compressive Antennas for High-Sensing-Capacity Applications

This letter presents a novel, model-based compressive antenna design method for high-sensing-capacity imaging applications. Given a set of design constraints, the method maximizes the capacity of the compressive antenna by varying the constitutive properties of scatterers distributed along the antenna. Preliminary two-dimensional design results demonstrate the new methods ability to produce antenna configurations with enhanced imaging capabilities.

A comparison of experimental and modeled results of an active millimeter wave inverse synthetic aperture radar system used to perform standoff detection of person-borne improvised explosive devices

With the recent rise in casualties resulting from person-borne improvised explosive devices (PBIEDs) or ”suicide bombers,” there is an urgent need for standoff detection of such threats. An optimum system that fulfills the requirements of standoff detection must be portable, low cost, and have a high probability of detection with low probability of false alarm at a distance of at least 20 meters. Currently there are a variety of modalities being researched to perform standoff detection of PBIEDs including: backscatter X-ray imaging, infrared imaging, optical detection, terahertz imaging, video analytics, and millimeter-wave (MMW) imaging. MMW imaging at 94 GHz is a very good modality for performing standoff detection of PBIEDs. MMWs can propagate through the atmosphere and clothing with very little attenuation, while at the same time do not cause damage to human skin tissue. A mono-static linear frequency modulated continuous wave (LFMCW) circular inverse synthetic aperture radar (ISAR) system has been developed and tested. A model of such a system using a two dimensional full wave analysis based on the finite difference method in the frequency domain has been developed and compared with results of the experimental system. Using a two dimensional matched filtering technique in the frequency domain, simulated images have been used as a means of performing target detection and classification. The imaging results of both simulated and experimentally obtained data is presented in this paper. Initial results using the 2D matched filtering target classification technique will also be presented.

Preliminary imaging results and SAR analysis of a microwave imaging system for early breast cancer detection

Currently X-ray-based imaging systems suffer from low contrast between malignant and healthy fibrous tissues in breast. Microwave Near-field Radar Imaging (NRI) shows a higher contrast between the aforementioned tissues and therefore can enhance tumor detection and diagnosis accuracy. In this work, we present the first imaging results of our developed NRI system that is equipped with a pair of Antipodal Vivaldi Antennas. We used a metal bearing ball immersed in oil as our object of interest, to keep the first measurement configuration simple. Moreover, to demonstrate the safety of our system for human subject tests, we simulated the Specific Absorption Rate (SAR) in a realistic breast tissue model and compared the resulted values with both the USA and Europe standards. The results show that firstly the imaging results from the measurements and simulations are comparable, and secondly the antennas radiations meet the SAR criteria.

3D Imaging of breast cancer using a hybrid Nearfield Radar Imaging/Digital Breast Tomosynthesis reconstruction technique

This paper extends previous work on a hybrid Digital-Breast-Tomosynthesis (DBT) and microwave Nearfield Radar Imaging (NRI) technique for detecting breast cancer to three dimensions. By using the DBT image to generate a background distribution of the tissues within the breast, the NRI algorithm is able to effectively image cancerous tumors within the breast.

Preliminary Results of a New Auxiliary Mechatronic Near-Field Radar System to 3D Mammography for Early Detection of Breast Cancer

Accurate and early detection of breast cancer is of high importance, as it is directly associated with the patients’ overall well-being during treatment and their chances of survival. Uncertainties in current breast imaging methods can potentially cause two main problems: (1) missing newly formed or small tumors; and (2) false alarms, which could be a source of stress for patients. A recent study at the Massachusetts General Hospital (MGH) indicates that using Digital Breast Tomosynthesis (DBT) can reduce the number of false alarms, when compared to conventional mammography. Despite the image quality enhancement DBT provides, the accurate detection of cancerous masses is still limited by low radiological contrast (about 1%) between the fibro-glandular tissue and affected tissue at X-ray frequencies. In a lower frequency region, at microwave frequencies, the contrast is comparatively higher (about 10%) between the aforementioned tissues; yet, microwave imaging suffers from low spatial resolution. This work reviews conventional X-ray breast imaging and describes the preliminary results of a novel near-field radar imaging mechatronic system (NRIMS) that can be fused with the DBT, in a co-registered fashion, to combine the advantages of both modalities. The NRIMS consists of two antipodal Vivaldi antennas, an XY positioner, and an ethanol container, all of which are particularly designed based on the DBT physical specifications. In this paper, the independent performance of the NRIMS is assessed by (1) imaging a bearing ball immersed in sunflower oil and (2) computing the heat Specific Absorption Rate (SAR) due to the electromagnetic power transmitted into the breast. The preliminary results demonstrate that the system is capable of generating images of the ball. Furthermore, the SAR results show that the system complies with the standards set for human trials. As a result, a configuration based on this design might be suitable for use in realistic clinical applications.

Metamaterial-based compressive reflector antenna optimization

This paper presents a new optimization method for metamaterial-based Compressive Reflector Antennas (CRAs). The method aims at maximizing the sensing capacity of the CRA by tuning the resonant frequencies of multiple ELC-based meta-materials. Preliminary numerical simulations using Compressive Sensing demonstrate that the CRA presents enhanced imaging capabilities when compared to those of the baseline design.