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

Reconstruction of Planar Multilayered Structures using Multiplicative-Regularized Contrast Source Inversion

 There is an increasing interest to have an access to hidden objects without making any destructive action. Such non-destructive method is able to give a picture of the inner part of the structure by measuring some external entities. The problem of reconstructing planar multilayered structures based on given scattering data is an inverse problem. Inverse problems are ill-posed, beside matrix inversion tools, a regularization procedure must be applied additionally. Multiplicative regularization was considered as an appropriate penalty method to solve this problem. The Gauss-Newton inversion method as an optimization procedure was used to find the permittivity values, which minimized some cost functions. Several dielectric layers with different thickness and profiles were observed. Some layers needed more discretization elements and more iteration steps to give the correct profiles.

Linear-Model-inspired Neural Network for Electromagnetic Inverse Scattering

Electromagnetic inverse scattering problems (ISPs) aim to retrieve permittivities of dielectric scatterers from the scattering measurement. It is often highly nonlinear, caus-ing the problem to be very difficult to solve. To alleviate the issue, this letter exploits a linear model-based network (LMN) learning strategy, which benefits from both model complexity and data learning. By introducing a linear model for ISPs, a new model with network-driven regular-izer is proposed. For attaining efficient end-to-end learning, the network architecture and hyper-parameter estimation are presented. Experimental results validate its superiority to some state-of-the-arts.Comment: 5 pages, 6 figures 3 table

Microwave Imaging Using CMOS Integrated Circuits with Rotating 4 × 4 Antenna Array on a Breast Phantom

A digital breast cancer detection system using 65 nm technology complementary metal oxide semiconductor (CMOS) integrated circuits with rotating 4 × 4 antenna array is presented. Gaussian monocycle pulses are generated by CMOS logic circuits and transmitted by a 4 × 4 matrix antenna array via two CMOS single-pole-eight-throw (SP8T) switching matrices. Radar signals are received and converted to digital signals by CMOS equivalent time sampling circuits. By rotating the 4 × 4 antenna array, the reference signal is obtained by averaging the waveforms from various positions to extract the breast phantom target response. A signal alignment algorithm is proposed to compensate the phase shift of the signals caused by the system jitter. After extracting the scattered signal from the target, a bandpass filter is applied to reduce the noise caused by imperfect subtraction between original and the reference signals. The confocal imaging algorithm for rotating antennas is utilized to reconstruct the breast image. A 1 cm3 bacon block as a cancer phantom target in a rubber substrate as a breast fat phantom can be detected with reduced artifacts

Study of microwave tomography measurement setup configurations for breast cancer detection based on breast compression

Microwave tomography (MT) measurement setups for different configurations based on breast compression are compared to classical circular measurement setups. Configurations based on compression allow measuring the evanescent component of the scattered field and lead to a compact measurement setup that allows direct image comparison with a standard mammography system. The different configurations are compared based on the singular value decomposition (SVD) of the radiation operator for a 2D TM case. This analysis allows determining under which conditions the image quality obtained from the reconstructions can be enhanced. These findings are confirmed by a series of reconstructions of breast phantoms based on synthetic data obtained at a single frequency of operation