Have a personal or library account? Click to login
Improving Image Quality in Electrical Impedance Tomography (EIT) Using Projection Error Propagation-Based Regularization (PEPR) Technique: A Simulation Study Cover

Improving Image Quality in Electrical Impedance Tomography (EIT) Using Projection Error Propagation-Based Regularization (PEPR) Technique: A Simulation Study

Journal of Electrical Bioimpedance
Volume 2 (2011): Issue 1 (January 2011)
By: Tushar Kanti Bera,  Samir Kumar Biswas,  K. Rajan and  J. Nagaraju  
Open Access
|Mar 2011

Figures & Tables

Fig.1

An EIT system with electrode array on patient under test.
An EIT system with electrode array on patient under test.

Fig.2

Resistivity imaging for object near electrode 1: (a) Original object, (b) with LMR, (c) with PEPR, (d) DRP of the images.
Resistivity imaging for object near electrode 1: (a) Original object, (b) with LMR, (c) with PEPR, (d) DRP of the images.

Fig.3

Resistivity imaging for object near electrode 3: (a) Original object, (b) with LMR, (c) with PEPR, (d) DRP of the images.
Resistivity imaging for object near electrode 3: (a) Original object, (b) with LMR, (c) with PEPR, (d) DRP of the images.

Fig.-4

Resistivity imaging for object near electrode 5: (a) Original object, (b) with LMR, (c) with PEPR, (d) DRP of the images.
Resistivity imaging for object near electrode 5: (a) Original object, (b) with LMR, (c) with PEPR, (d) DRP of the images.

Fig.-5

Resistivity images with noisy (25 %) boundary data (object near electrode 1): (a) with LMR, (b) with PEPR, (c) DRP of the reconstructed image
Resistivity images with noisy (25 %) boundary data (object near electrode 1): (a) with LMR, (b) with PEPR, (c) DRP of the reconstructed image

Fig.-6

Resistivity images with noisy (25 %) boundary data (object near electrode 3): (a) with LMR, (b) with PEPR, (c) DRP of the reconstructed image
Resistivity images with noisy (25 %) boundary data (object near electrode 3): (a) with LMR, (b) with PEPR, (c) DRP of the reconstructed image

Fig.-7

Resistivity images with noisy (25 %) boundary data (object near electrode 5): (a) with LMR, (b) with PEPR, (c) DRP of the reconstructed image
Resistivity images with noisy (25 %) boundary data (object near electrode 5): (a) with LMR, (b) with PEPR, (c) DRP of the reconstructed image

Fig.-8

Resistivity images obtained from boundary data with 10 % noise (object near electrode 1): (a) image with LMR, (b) DRP of the reconstructed image shown in Fig.-1a, (c) image with PEPR, (d) DRP of the reconstructed image shown in Fig.-1c.
Resistivity images obtained from boundary data with 10 % noise (object near electrode 1): (a) image with LMR, (b) DRP of the reconstructed image shown in Fig.-1a, (c) image with PEPR, (d) DRP of the reconstructed image shown in Fig.-1c.

Fig.-9

Resistivity images obtained from noisy boundary data (Error added = 10 %) with LMR (λ = 0.01) method (object near electrode 1) for first twelve iterations: (a) to (l) images represents the reconstruction of 1 to 12 iterations respectively.
Resistivity images obtained from noisy boundary data (Error added = 10 %) with LMR (λ = 0.01) method (object near electrode 1) for first twelve iterations: (a) to (l) images represents the reconstruction of 1 to 12 iterations respectively.

Fig.-10

DRP of the resistivity images shown in Fig.-9 (reconstruction with LMR): with noisy data (object near electrode 3): (a) to (l) images represents the DRP of the images shown in Fig.-9a to Fig.-9l respectively.
DRP of the resistivity images shown in Fig.-9 (reconstruction with LMR): with noisy data (object near electrode 3): (a) to (l) images represents the DRP of the images shown in Fig.-9a to Fig.-9l respectively.

Fig.-11

Resistivity images obtained from noisy boundary data (Error added = 10 %) with PEPR (Ψ = 0.01) method (object near electrode 1) for first twelve iterations: (a) to (l) images represents the reconstruction of 1 to 12 iterations respectively.
Resistivity images obtained from noisy boundary data (Error added = 10 %) with PEPR (Ψ = 0.01) method (object near electrode 1) for first twelve iterations: (a) to (l) images represents the reconstruction of 1 to 12 iterations respectively.

Fig.-12

DRP of the resistivity images shown in Fig.-11 (reconstruction with PEPR): with noisy data (object near electrode 1): (a) to (l) images represents the DRP of the images shown in Fig.-11a to Fig.-11l respectively.
DRP of the resistivity images shown in Fig.-11 (reconstruction with PEPR): with noisy data (object near electrode 1): (a) to (l) images represents the DRP of the images shown in Fig.-11a to Fig.-11l respectively.

Fig.-13

Reconstruction parameters and reconstruction errors for the resistivity images obtained from noisy boundary data (Error added = 10 %) with LMR (λ = 0.01) and PEPR (Ψ = 0.01) methods (object near electrode 1): (a) maximum values of the reconstructed inhomogeneity resistivity (IRMax), (b) mean of reconstructed inhomogeneity resistivity (IRMean).
Reconstruction parameters and reconstruction errors for the resistivity images obtained from noisy boundary data (Error added = 10 %) with LMR (λ = 0.01) and PEPR (Ψ = 0.01) methods (object near electrode 1): (a) maximum values of the reconstructed inhomogeneity resistivity (IRMax), (b) mean of reconstructed inhomogeneity resistivity (IRMean).

Fig.-14

Reconstruction parameters and reconstruction errors for the resistivity images obtained from noisy boundary data (Error added = 10 %) with LMR ( λ = 0.01) and PEPR ( Ψ = 0 .01) methods (object near electrode 1): (a) projection error (EV), (b) normalized solution error norm (Eρ).
Reconstruction parameters and reconstruction errors for the resistivity images obtained from noisy boundary data (Error added = 10 %) with LMR ( λ = 0.01) and PEPR ( Ψ = 0 .01) methods (object near electrode 1): (a) projection error (EV), (b) normalized solution error norm (Eρ).

Fig.-15

Projection errors (EV) calculated in the resistivity reconstruction at different iterations (a) with LMR (λ = 1 to 0.0001), (b) with PEPR (Ψ = 1 to 0.0001).
Projection errors (EV) calculated in the resistivity reconstruction at different iterations (a) with LMR (λ = 1 to 0.0001), (b) with PEPR (Ψ = 1 to 0.0001).

CNR, PCR and COC of reconstructed images for noisy data (object near electrode 1)

RegularizationCNRPCRCOC
LMR1.8127.291.85
PEPR3.1763.792.90

CNR, PCR and COC of reconstructed images for noisy data (object near electrode 3)

RegularizationCNRPCRCOC
LMR1.6023.611.73
PEPR1.8935.682.12

CNR, PCR and COC of reconstructed images of the object near electrode 5_

RegularizationCNRPCRCOC
LMR2.9230.321.98
PEPR3.4644.742.40

CNR, PCR and COC of reconstructed images of the object near electrode 3

RegularizationCNRPCRCOC
LMR2.9931.332.01
PEPR3.5146.262.45

CNR, PCR and COC of reconstructed images for noisy data (object near electrode 5)

RegularizationCNRPCRCOC
LMR1.2417.591.54
PEPR1.4825.911.78

CNR, PCR and COC of reconstructed images of the object near electrode 1

RegularizationCNRPCRCOC
LMR3.0832.522.05
PEPR3.5546.572.46

Projection errors (EV) calculated for different values of λ in LMR and Ψ in PEPR_

λ or Ψ at 4th IterationLMRPEPR
1.051.824853.3685
0.151.384451.7703
0.0151.501651.3234
0.00155.947351.3802
0.000170.839456.8654

CNR, PCR, COC, IRMax and IRMean of reconstructed images shown in Fig_-8_

RegularizationCNRPCRCOCIRMaxIRMean
LMR2.9589.152.9077.0341.46
PEPR3.2646.482.4933.9623.70
DOI: https://doi.org/10.5617/jeb.158 | Journal eISSN: 1891-5469
Journal RSS Feed
Language: English
Page range: 2 - 12
Submitted on: Jan 31, 2011
Published on: Mar 13, 2011
Published by: University of Oslo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
electrical impedance tomography,
projection error propagation-based regularization (PEPR),
simulated boundary potentials,
image reconstruction,
forward problem,
inverse problem,
finite element method
Related subjects:
Engineering,
Bioengineering and biomedical engineering,
Biomedical electronics,
Life sciences,
Biophysics,
Medicine,
Biomedical engineering,
Physics,
Spectroscopy and metrology

© 2011 Tushar Kanti Bera, Samir Kumar Biswas, K. Rajan, J. Nagaraju, published by University of Oslo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Previous articleVolume 2 (2011): Issue 1 (January 2011)Next article