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 algo...

Full description

Saved in:
Bibliographic Details
Published in:Physiological measurement 2009-06, Vol.30 (6), p.S19-S34
Main Authors: Kulkarni, Rujuta, Kao, Tzu-Jen, Boverman, Gregory, Isaacson, David, Saulnier, Gary J, Newell, Jonathan C
Format: Article
Language:English
Subjects:
Breast Neoplasms - diagnosis
Electric Impedance
Equipment Design
Female
Fourier Analysis
Humans
Image Processing, Computer-Assisted
Models, Biological
Tomography - instrumentation
Tomography - methods
Tomography - statistics & numerical data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0967-3334
1361-6579
DOI:10.1088/0967-3334/30/6/S02