A two-layered forward model of tissue for electrical impedance tomography.
Electrical impedance tomography is being explored as a technique to detect breast cancer, exploiting the differences in admittivity between normal tissue and tumors. In this paper, the geometry is modeled as an infinite half space under a hand-held probe. A forward solution and a reconstruction algorithm for this geometry were developed previously by Mueller et al (1999 IEEE Trans. Biomed. Eng. 46 1379). In this paper, we present a different approach which uses the decomposition of the forward solution into its Fourier components to obtain the forward solution and the reconstructions. The two approaches are compared in terms of the forward solutions and the reconstructions of experimental tank data. We also introduce a two-layered model to incorporate the presence of the skin that surrounds the body area being imaged. We demonstrate an improvement in the reconstruction of a target in a layered medium using this layered model with finite difference simulated data. We then extend the application of our layered model to human subject data and estimate the skin and the tissue admittivities for data collected on the human abdomen using an ultrasound-like hand-held EIT probe. Lastly, we show that for this set of human subject data, the layered model yields an improvement in predicting the measured voltages of around 81% for the lowest temporal frequency (3 kHz) and around 61% for the highest temporal frequency (1 MHz) applied when compared to the homogeneous model.
Kulkarni
Rujuta
R
Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Kao
Tzu-Jen
TJ
Boverman
Gregory
G
Isaacson
David
D
Saulnier
Gary J
GJ
Newell
Jonathan C
JC
D001943:Breast Neoplasms / Q000175:diagnosis; D017097:Electric Impedance; D004867:Equipment Design; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008954:Models, Biological; D014054:Tomography / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data