Breast Microwave Radar(BMR) is a novel imaging modality that is capable of producing high contrast images and can detect tumors of at least 4mm. To properly visualize the responses from the breast structures, BMR data sets must be reconstructed. In this paper, a real time BMR image formation technique is proposed. This approach is based on the use of a Single Instruction Multiple Thread(SIMT) interpolation method. By using this programming model, the proposed approach can be implemented on General Purpose Graphic Processing Unit (GPGPU) platform to speed up the reconstruction process. The proposed method yielded promising results when applied to simulated data sets obtained using anatomically accurate numeric phantoms. In average, the proposed approach yielded speed increases of one order of magnitude compared to its CPU counterpart, and two orders of magnitude with respect to current BMR reconstruction techniques.

In recent years, the use of radar technology has been proposed in a wide range of subsurface imaging applications. Traditionally, linear scan trajectories are used to acquire data in most subsurface radar applications. However, novel applications, such as breast microwave imaging and wood inspection, require the use of nonlinear scan trajectories in order to adjust to the geometry of the scanned area. This paper proposes a novel reconstruction algorithm for subsurface radar data acquired along cylindrical scan trajectories. The spectrum of the collected data is processed in order to locate the spatial origin of the target reflections and remove the spreading of the target reflections which results from the different signal travel times along the scan trajectory. The proposed algorithm was successfully tested using experimental data collected from phantoms that mimic high contrast subsurface radar scenarios, yielding promising results. Practical considerations such as spatial resolution and sampling constraints are discussed and illustrated as well.

Breast cancer, with the exception of lung cancer, is the leading cause of cancer deaths in women. It is also one of the few cancers that can be controlled by using asymptomatic screening method, followed by effective treatments. One recent screening modality under development, microwave tomography, uses the apparent dielectric property contrasts between different breast tissues at microwave frequencies. Microwave tomography uses a numerical model and the image reconstruction consists of iteratively searching the breast structures, applying the numerical model to the breast structures, and matching measured data with computation results of the model. This paper focuses on finite-difference time-domain (FDTD) for the numerical model and genetic algorithm (GA) for the iterative searches. FDTD and GA are time-consuming, yet they are data parallel in nature. In this paper, a parallel algorithm integrating GA and FDTD for detecting tumors using microwave tomography technique is presented. The algorithm is implemented on distributed memory machines using MPI.

Different technologies have been used for breast cancer detections clinically. But they have weaknesses in terms of sensitivity and specificity. Microwave imaging technique, on the contrary, uses the apparent dielectric property contrasts between different breast tissues at microwave frequencies and is a prospective direction to find small tumor at their early stage. Microwave tomography falls in one category of microwave imaging technique. There are two main components in microwave tomography to detect abnormalities in breasts: genetic algorithm (GA) and finite-difference time-domain (FDTD). Both GA and FDTD are time-consuming, but, they are data-parallel in nature. In this paper, we have designed a parallel framework for microwave tomography: parallel GA combined with parallel FDTD. The algorithms are implemented on distributed memory machines running MPI. The execution time of the sequential algorithm (GA and FDTD combined) is 10,131 seconds. The total execution time obtained on 16 processors which is approximately 2000 seconds surpasses the sequential algorithm.