Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD).

Abstract

The comparison between breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation time distribution (EIT-GRTD) and conventional EIT has been conducted to evaluate the optimal frequency for cancer detection <i>f</i> ^cancer. The EIT-GRTD has two steps, which are 1) the determination of the <i>f</i> ^cancer and 2) the refinement of breast reconstruction through time-constant enhancement. This paper employs two-dimensional numerical simulations by a finite element method (FEM) software to replicate the process of breast cancer recognition. The simulation is constructed based on two distinct electrical properties, which are conductivity <i>σ</i> and permitivitty <i>ε</i>, inherent to two major breast tissues: adipose tissues, and breast cancer tissues. In this case, the <i>σ</i> and <i>ε</i> of breast cancer <i>σ</i> ^cancer, <i>ε</i> ^cancer are higher than adipose tissues <i>σ</i> ^adipose, <i>ε</i> ^adipose. The simulation results indicate that the most effective frequency for breast cancer detection based on EIT-GRTD is <i>f</i> ^cancer = 56,234 Hz. Meanwhile, conventional EIT requires more processing to determine the <i>f</i> ^cancer based on image results or spatial conductivity analysis. Quantitatively, both EIT-GRTD and conventional EIT can clearly show the position of the cancer in layers 1 and 2 for EIT-GRTD and only layer 1 for conventional EIT.