Beamspace time reversal maximum likelihood estimation for microwave breast imaging

© 2014 IEEE. We consider maximum likelihood based beamspace time reversal beamforming for breast cancer localization. We reduce the computational burden of maximum likelihood estimation through reduced dimensional beamspace processing. Beamspace processing also provides additional beamspace gain which contributes to suppress strong clutter effects. We collect multistatic scattering fields through FDTD simulation and further process it in beamspace for maximum likelihood based time reversal imaging. The imaging technique is used to localize a small tumor in a dense breast. It is observed that the proposed imaging technique can localize tumors unambiguously even in dense breast phantom

Breast cancer detection in highly dense numerical breast phantoms using time reversal

In this paper we investigate the detection of breast cancer using two-dimensional slices of realistic numerical phantoms employing time reversal microwave imaging. We used maximum-likelihood estimation coupled with time reversal technique to detect and estimate the location of tumor using FDTD based breast phantoms that contain dense fibroglandular tissue clutter. We show that time reversal maximum-likelihood estimation can detect and accurately localize tumors even in highly dense breasts where the dielectric contrast between healthy dense breast tissue and cancerous lesions is quite low without requiring any contrast enhancing agents. © 2013 IEEE

Improved DORT for breast cancer detection in low contrast scenarios

© 2015 The Institute of Electronics, Information and Comm. Microwave imaging performance deteriorates with increasing clutter and heterogeneity in the imaging medium. Breast cancer detection becomes increasingly challenging with increasing breast density. Decomposition of the time reversal operator (DORT) uses signal subspace of the multistatic matrix which is perturbed in highly heterogeneous medium. To overcome the problem we propose coherent processing in frequency domain prior to imaging operation. Coherent DORT (C-DORT) provides robust imaging performance compared to conventional non-coherent DORT in cluttered medium as evident from the imaging results obtain using anatomically realistic numerical breast phantoms

Breast cancer detection using interferometric MUSIC: Experimental and numerical assessment

Purpose: In microwave breast cancer detection, it is often beneficial to arrange sensors in close proximity to the breast. The resultant coupling generally changes the antenna response. As an a priori characterization of the radio frequency system becomes difficult, this can lead to severe degradation of the detection efficacy. The purpose of this paper is to demonstrate the advantages of adopting an interferometric multiple signal classification (I-MUSIC) approach due to its limited dependence from a priori information on the antenna. The performance of I-MUSIC detection was measured in terms of signal-to-clutter ratio (SCR), signal-to-mean ratio (SMR), and spatial displacement (SD) and compared to other common linear noncoherent imaging methods, such as migration and the standard wideband MUSIC (WB-MUSIC) which also works when the antenna is not accounted for. Methods: The data were acquired by scanning a synthetic oil-in-gelatin phantom that mimics the dielectric properties of breast tissues across the spectrum 1–3 GHz using a proprietary breast microwave multi-monostatic radar system. The phantom is a multilayer structure that includes skin, adipose, fibroconnective, fibroglandular, and tumor tissue with an adipose component accounting for 60% of the whole structure. The detected tumor has a diameter of 5 mm and is inserted inside a fibroglandular region with a permittivity contrast εr-tumor/εr-fibroglandular \u3c 1.5 over the operating band. Three datasets were recorded corresponding to three antennas with different coupling mechanisms. This was done to assess the independence of the I-MUSIC method from antenna characterizations. The datasets were processed by using I-MUSIC, noncoherent migration, and wideband MUSIC under equivalent conditions (i.e., operative bandwidth, frequency samples, and scanning positions). SCR, SMR, and SD figures were measured from all reconstructed images. In order to benchmark experimental results, numerical simulations of equivalent scenarios were carried out by using CST Microwave Studio. The three numerical datasets were then processed following the same procedure that was designed for the experimental case. Results: Detection results are presented for both experimental and numerical phantoms, and higher performance of the I-MUSIC method in comparison with the WB-MUSIC and noncoherent migration is achieved. This finding is confirmed for the three different antennas in this study. Although a delocalization effect occurs, experimental datasets show that the signal-to-clutter ratio and the signal-to-mean performance with the I-MUSIC are at least 5 and 2.3 times better than the other methods, respectively. The numerical datasets calculated on an equivalent phantom for cross-testing confirm the improved performance of the I-MUSIC in terms of SCR and SMR. In numerical simulations, the delocalization effect is dramatically reduced up to an SD value of 1.61 achieved with the I-MUSIC in combination with the antipodal Vivaldi antenna. This shows that mechanical uncertainties are the main reason for the delocalization effect in the measurements. Conclusions: Experimental results show that the I-MUSIC generates images with signal-to-clutter levels higher than 5.46 dB across all working conditions and it reaches 7.84 dB in combination with the antipodal Vivaldi antenna. Numerical simulations confirm this trend and due to ideal mechanical conditions return a signal-to-clutter level higher than 7.61 dB. The I-MUSIC largely outperforms the methods under comparison and is able to detect a 5-mm tumor with a permittivity contrast of 1.5

A Robust and Artifact Resistant Algorithm of Ultrawideband Imaging System for Breast Cancer Detection.

Goal: Ultrawideband radar imaging is regarded as one of the most promising alternatives for breast cancer detection. A range of algorithms reported in literature show satisfactory tumor detection capabilities. However, most of algorithms suffer significant deterioration or even fail when the early-stage artifact, including incident signals and skin-fat interface reflections, cannot be perfectly removed from received signals. Furthermore, fibro-glandular tissue poses another challenge for tumor detection, due to the small dielectric contrast between glandular and cancerous tissues. Methods: This paper introduces a novel Robust and Artifact Resistant (RAR) algorithm, in which a neighborhood pairwise correlation-based weighting is designed to overcome the adverse effects from both artifact and glandular tissues. In RAR, backscattered signals are time-shifted, summed, and weighted by the maximum combination of the neighboring pairwise correlation coefficients between shifted signals, forming the intensity of each point within an imaging area. Results: The effectiveness was investigated using 3-D anatomically and dielectrically accurate finite-difference-time-domain numerical breast models. The use of neighborhood pairwise correlation provided robustness against artifact, and enabled the detection of multiple scatterers. RAR is compared with four well-known algorithms: delay-and-sum, delay-multiply-and-sum, modified-weighted-delay-and-sum, and filtered-delay-and-sum. Conclusion: It has shown that RAR exhibits improved identification capability, robust artifact resistance, and high detectability over its counterparts in most scenarios considered, while maintaining computational efficiency. Simulated tumors in both homogeneous and heterogonous, from mildly to moderately dense breast phantoms, combining an entropy-based artifact removal algorithm, were successfully identified and localized. Significance: These results show the strong potential of RAR for breast cancer screening

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Design of Miniaturized Antipodal Vivaldi Antennas and a Microwave Head Imaging System for the Detection of Blood Clots in the Brain

Traditional brain imaging modalities, for example, MRI, CT scan, X-ray, etc. can provide precise and high-resolution images of the brain for diagnosing lesions, tumors or clots inside the brain. However, these modalities require bulky and expensive test setups accessible only at specialized diagnostic centers, and hence may not be suitable or affordable to many patients. Furthermore, the inherent health risks limit the usability of these modalities for frequent monitoring. Microwave imaging is deemed a promising alternative due to its being cost-effective, portable, non-ionizing, non-intrusive. Therefore, this work aims to design an effective microwave head imaging system for the detection of blood clots inside the brain. Two miniaturized antipodal Vivaldi antenna designs are proposed which can provide wideband operation covering the low microwave frequency range (within 1 - 6 GHz) while having electrically small dimensions, directional radiation pattern with reasonable gain, and without requiring immersion in any matching/ coupling liquid. A head imaging system is presented which utilizes a quarter-head scanning approach, to reconstruct four images of the brain by scanning four quarters of the head, using the designed antipodal wideband Vivaldi antenna. A numerical brain model, with and without the presence of blood clot, is simulated using the proposed head-imaging system. At each quarter, the antenna is placed at nine different positions for scanning. The reflected signal at each position is processed and using confocal microwave imaging technique four images of the brain are reconstructed. A comparison is made among the four images in terms of their intensities, for the detection and approximate location of the blood clot inside the brain. The presence of higher intensity regions in any specific quarter of the head demonstrates the presence of a clot and its location and validates the feasibility of the proposed head imaging system using the low frequency wideband Vivaldi antenna