<abstract xmlns="http://www.w3.org/1999/xhtml"><p>A Block Matrix based Multiple Regularization (BMMR) technique is proposed for improving conductivity image quality in Electrical Impedance Tomography (EIT). The response matrix (J<sup>T</sup>J) has been partitioned into several sub-block matrices and the largest element of each sub-block matrix has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domains with circular inhomogeneities of different geometry and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR) and modified Levenberg-Marquardt Regularization (LMR) methods. Results show that the BMMR technique improves the impedance image and its spatial resolution for single and multiple inhomogeneity phantoms of different geometries. It is observed that the BMMR technique reduces the projection error as well as the solution error and improves the conductivity reconstruction in EIT. Results also show that the BMMR method improves the image contrast and inhomogeneity conductivity profile by reducing background noise for all the phantom configurations.</p></abstract>

A Block Matrix based Multiple Regularization (BMMR) technique is proposed for improving conductivity image quality in Electrical Impedance Tomography (EIT). The response matrix (JTJ) has been partitioned into several sub-block matrices and the largest element of each sub-block matrix has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domains with circular inhomogeneities of different geometry and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR) and modified Levenberg-Marquardt Regularization (LMR) methods. Results show that the BMMR technique improves the impedance image and its spatial resolution for single and multiple inhomogeneity phantoms of different geometries. It is observed that the BMMR technique reduces the projection error as well as the solution error and improves the conductivity reconstruction in EIT. Results also show that the BMMR method improves the image contrast and inhomogeneity conductivity profile by reducing background noise for all the phantom configurations.

Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

Journal of Electrical Bioimpedance

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Block Matrix based Multiple Regularization (BMMR) technique is proposed for improving conductivity image quality in Electrical Impedance Tomography (EIT)....

Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

Article Category: Articles

Publié en ligne: 15 juin 2011

Pages: 33 - 47

Reçu: 30 mars 2011

DOI: https://doi.org/10.5617/jeb.170

Mots clés
EIT, MoBIIR, Jacobian, Block Matrix-based Multiple Regularization (BMMR), simulated boundary data, conductivity imaging, STR, LMR, normalized projection error, normalized solution error

© 2011 Tushar Kanti Bera, Samir Kumar Biswas, K. Rajan, J. Nagaraju, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

Article Category: Articles

Publié en ligne: 15 juin 2011

Pages: 33 - 47

Reçu: 30 mars 2011

DOI: https://doi.org/10.5617/jeb.170

Mots clésEIT, MoBIIR, Jacobian, Block Matrix-based Multiple Regularization (BMMR), simulated boundary data, conductivity imaging, STR, LMR, normalized projection error, normalized solution error

© 2011 Tushar Kanti Bera, Samir Kumar Biswas, K. Rajan, J. Nagaraju, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
EIT, MoBIIR, Jacobian, Block Matrix-based Multiple Regularization (BMMR), simulated boundary data, conductivity imaging, STR, LMR, normalized projection error, normalized solution error