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In this paper, we describe a 2-D wideband microwave imaging system intended for biomedical imaging. The system is capable of collecting data from 3 to 6 GHz, with 24 coresident antenna elements connected to a vector network analyzer via a 2 x 24 port matrix switch. As one of the major sources of error in the data collection process is a result of the strongly coupling 24 coresident antennas, we provide a novel method to avoid the frequencies where the coupling is large enough to prevent successful imaging. Through the use of two different nonlinear reconstruction schemes, which are an enhanced version of the distorted born iterative method and the multiplicative regularized contrast source inversion method, we show imaging results from dielectric phantoms in free space. The early inversion results show that with the frequency selection procedure applied, the system is capable of quantitatively reconstructing dielectric objects, and show that the use of the wideband data improves the inversion results over single-frequency data.

We describe a 2D wide-band multi-frequency microwave imaging system intended for biomedical imaging. The system is capable of collecting data from 2-10 GHz, with 24 antenna elements connected to a vector network analyzer via a 2 x 24 port matrix switch. Through the use of two different nonlinear reconstruction schemes: the Multiplicative-Regularized Contrast Source Inversion method and an enhanced version of the Distorted Born Iterative Method, we show preliminary imaging results from dielectric phantoms where data were collected from 3-6 GHz. The early inversion results show that the system is capable of quantitatively reconstructing dielectric objects.

The authors recently presented a novel microwave tomography method for creating quantitative images of the electromagnetic properties of the interior of unknown objects [1]. This method is based on a time-domain inverse solver which uses the multi-illumination technique and includes the dispersive and heterogeneous characteristic of the object. The Frequency Dependent Finite Difference Time Domain ((FD)²TD) and Genetic Algorithm (GA) technique were utilized for determining unknown characteristics of the object. In the present paper, the calibration of measured data are described and image reconstruction results for preliminary experiments performed at the University of Manitoba's Microwave Tomography Laboratory and at the Institut Frsenel are presented. [ABSTRACT FROM AUTHOR]

The robustness of the microwave tomography method based on frequency dependent finite difference time domain numerical method and hybrid genetic algorithm for breast cancer imaging for different levels of noise are investigated in this paper. These results indicate the algorithm performs well in the case of data contaminated by various levels of additive white Gaussian noise (up to 15 % of signal strength). Noise levels above this value inhibit the efficacy of the method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel microwave tomography system, which utilizes 24 double-layered Vivaldi antennas, each of which is equipped with a diode-loaded printed-wire probe. By biasing the probe's diodes, the impedance of the probe is modified, allowing an indirect measurement of the electric field at the probe's locations. Each printed-wire probe is loaded with five equally spaced p-i-n diodes, in series. We show that electric field data collected in this way within the proposed tomography system can be used to reconstruct the dielectric properties of an object of interest. Reconstructions for various objects are shown. Although the results are still preliminary, sufficient experimentation has been done to delineate the advantages of such an indirect method of collecting scattered-field data for tomographic imaging purposes. [ABSTRACT FROM PUBLISHER]

Abstract: Microwave tomography (MT) is a safe screening modality that can be used for breast cancer detection. The technique uses the dielectric property contrasts between different breast tissues at microwave frequencies to determine the existence of abnormalities. Our proposed MT approach is an iterative process that involves two algorithms: Finite-Difference Time-Domain (FDTD) and Genetic Algorithm (GA). It is a compute intensive problem: (i) the number of iterations can be quite large to detect small tumors; (ii) many fine-grained computations and discretizations of the object under screening are required for accuracy. In our earlier work, we developed a parallel algorithm for microwave tomography on CPU-based homogeneous, multi-core, distributed memory machines. The performance improvement was limited due to communication and synchronization latencies inherent in the algorithm. In this paper, we exploit the parallelism of microwave tomography on the Cell BE processor. Since FDTD is a numerical technique with regular memory accesses, intensive floating point operations and SIMD type operations, the algorithm can be efficiently mapped on the Cell processor achieving significant performance. The initial implementation of FDTD on Cell BE with 8 SPEs is 2.9 times faster than an eight node shared memory machine and 1.45 times faster than an eight node distributed memory machine. In this work, we modify the FDTD algorithm by overlapping computations with communications during asynchronous DMA transfers. The modified algorithm also orchestrates the computations to fully use data between DMA transfers to increase the computation-to-communication ratio. We see 54% improvement on 8 SPEs (27.9% on 1 SPE) for the modified FDTD in comparison to our original FDTD algorithm on Cell BE. We further reduce the synchronization latency between GA and FDTD by using mechanisms such as double buffering. We also propose a performance prediction model based on DMA transfers, number of instructions and operations, the processor frequency and DMA bandwidth. We show that the execution time from our prediction model is comparable (within 0.5 s difference) with the execution time of the experimental results on one SPE. [Copyright &y& Elsevier]