TITLE:
3-D EIT Image Reconstruction Using a Block-Based Compressed Sensing Approach
AUTHORS:
Lan-Rong Dung, Chian-Wei Yang, Yin-Yi Wu
KEYWORDS:
EIT, Compressed Sensing, Image Reconstruction
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.13,
November
19,
2014
ABSTRACT: Electrical impedance tomography (EIT) is a
fast and cost-effective technique that provides a tomographic conductivity
image of a subject from boundary current-voltage data. This paper proposes a
time and memory efficient method for solving a large scale 3D EIT image
reconstruction problem and the ill-posed linear inverse problem. First, we use
block-based sampling for a large number of measured data from many electrodes.
This method will reduce the size of Jacobian matrix and can improve accuracy of
reconstruction by using more electrodes. And then, a sparse matrix reduction
technique is proposed using thresholding to set very small values of the
Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format,
the element with zeros would be eliminated, which results in a saving of memory
requirement. Finally, we built up the relationship between compressed sensing
and EIT definitely and induce the CS: two-step Iterative Shrinkage/Thresholding
and block-based method into EIT image reconstruction algorithm. The results
show that block-based compressed sensing enables the large scale 3D EIT problem
to be efficient. For a 72-electrodes EIT system, our proposed method could save
at least 61% of memory and reduce time by 72% than compressed sensing method
only. The improvements will be obvious by using more electrodes. And this
method is not only better at anti-noise, but also faster and better resolution.