Image Reconstruction using Microwave Tomography for Breast Cancer Detection on Distributed Memory Machine

Abstract

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.

Finite-difference time-domain on the cell/B.E. processor

“…The relationships between E and H are manifested in equation (1) to equation (4) for 2D space. We refer readers to the paper [14] for a detailed description of the medical imaging application behind the research and the computational domain. Let α =…”

“…Xu et al [14] develop a distributed FDTD algorithm on a cluster for microwave tomography in breast cancer detection. The experiment results indicate that the cluster is not a very suitable architecture for FDTD which consumes the most computation power in the integrated algorithm consisting of FDTD and GA.…”

“…FDTD is usually a kernel in many of the applications such as microwave tomography [14]. It is a computationally intensive and time consuming task.…”

Microwave tomography for breast cancer detection on Cell broadband engine processors

“…There are two issues that make this approach a compute intensive problem: (i) the number of iterations can be quite large to detect small tumors; (ii) the object has to be discretized into fine-grained computations for accuracy. In [35], we developed a parallel algorithm for microwave tomography and parallelized the algorithm on CPUbased homogeneous, multi-core, distributed memory machine. The results indicate that the communication and synchronization latencies add significant overhead to the entire performance of the algorithm.…”

UWB high-contrast robust tomographic imaging for medical applications

“…With the advent of cost effective high performance computing systems, a significant research effort in reconstruction algorithms for breast detection purposes has been made, i.e. confocal imaging techniques [3], non-linear optimization methods [4], and Fourier imaging techniques. While much progress has been made in reconstruction algorithms applied to more and more realistic breast phantom simulations, hardly ever have been tested with experimental measurements.…”