NDLI: A comparison between compressed sensing algorithms in Electrical Impedance Tomography

Content Provider	IEEE Xplore Digital Library
Author	Tehrani, J.N. Jin, C. McEwan, A. van Schaik, A.
Copyright Year	2010
Description	Author affiliation: School of Electrical and Information Engineering, The University of Sydney, Australia, NSW 2006 (Tehrani, J.N.; Jin, C.; McEwan, A.; van Schaik, A.)
Abstract	Electrical Impedance Tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. Conventional EIT reconstruction methods solve a linear model by minimizing the least squares error, i.e., the Euclidian or L2-norm, with regularization. Compressed sensing provides unique advantages in Magnetic Resonance Imaging (MRI) [1] when the images are transformed to a sparse basis. EIT images are generally sparser than MRI images due to their lower spatial resolution. This leads us to investigate ability of compressed sensing algorithms currently applied to MRI in EIT without transformation to a new basis. In particular, we examine four new iterative algorithms for L1 and L0 minimization with applications to compressed sensing and compare these with current EIT inverse L1-norm regularization methods. The four compressed sensing methods are as follows: (1) an interior point method for solving L1-regularized least squares problems (L1-LS); (2) total variation using a Lagrangian multiplier method (TVAL3); (3) a two-step iterative shrinkage / thresholding method (TWIST) for solving the L0-regularized least squares problem; (4) The Least Absolute Shrinkage and Selection Operator (LASSO) with tracing the Pareto curve, which estimates the least squares parameters subject to a L1-norm constraint. In our investigation, using 1600 elements, we found all four CS algorithms provided an improvement over the best conventional EIT reconstruction method, Total Variation, in three important areas: robustness to noise, increased computational speed of at least 40x and a visually apparent improvement in spatial resolution. Out of the four CS algorithms we found TWIST was the fastest with at least a 100x speed increase.
Starting Page	3109
Ending Page	3112
File Size	655395
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627165
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tomography Impedance Compressed sensing Finite element methods Noise TV Noise level
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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