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

2019 ◽  
Vol 2 (1) ◽  
pp. 33-47 ◽  
Author(s):  
Tushar Kanti Bera ◽  
Samir Kumar Biswas ◽  
K. Rajan ◽  
J. Nagaraju
Keyword(s):  
Image Quality ◽  
Electrical Impedance ◽  
Regularization Parameter ◽  
Single Step ◽  
Block Matrix ◽  
Impedance Tomography ◽  
Conductivity Profile ◽  
Circular Domains ◽  
Conductivity Image ◽  
Projection Error

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.

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

2019 ◽  
Vol 2 (1) ◽  
pp. 2-12 ◽  
Author(s):  
Tushar Kanti Bera ◽  
Samir Kumar Biswas ◽  
K. Rajan ◽  
J. Nagaraju
Keyword(s):  
Image Quality ◽  
Electrical Impedance Tomography ◽  
Error Propagation ◽  
Electrical Impedance ◽  
Measured Data ◽  
Response Matrix ◽  
Reconstruction Accuracy ◽  
Impedance Tomography ◽  
Reconstruction Process ◽  
Projection Error

Abstract A Projection Error Propagation-based Regularization (PEPR) method is proposed and the reconstructed image quality is improved in Electrical Impedance Tomography (EIT). A projection error is produced due to the misfit of the calculated and measured data in the reconstruction process. The variation of the projection error is integrated with response matrix in each iteration and the reconstruction is carried out in EIDORS. The PEPR method is studied with the simulated boundary data for different inhomogeneity geometries. Simulated results demonstrate that the PEPR technique improves image reconstruction precision in EIDORS and hence it can be successfully implemented to increase the reconstruction accuracy in EIT.

scholarly journals Numerical determination of anomalies in multifrequency electrical impedance tomography

2018 ◽  
Vol 30 (3) ◽  
pp. 481-504 ◽  
Author(s):  
HABIB AMMARI ◽  
FAOUZI TRIKI ◽  
CHUN-HSIANG TSOU
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Extreme Case ◽  
Cauchy Data ◽  
Perfect Conductor ◽  
Impedance Tomography ◽  
Piecewise Constant ◽  
Conductivity Distribution ◽  
Conductivity Profile

The multifrequency electrical impedance tomography consists in retrieving the conductivity distribution of a sample by injecting a finite number of currents with multiple frequencies. In this paper, we consider the case where the conductivity distribution is piecewise constant, takes a constant value outside a single smooth anomaly, and a frequency dependent function inside the anomaly itself. Using an original spectral decomposition of the solution of the forward conductivity problem in terms of Poincaré variational eigenelements, we retrieve the Cauchy data corresponding to the extreme case of a perfect conductor, and the conductivity profile. We then reconstruct the anomaly from the Cauchy data. The numerical experiments are conducted using gradient descent optimization algorithms.

scholarly journals CoReHA 2.0: A Software Package forIn VivoMREIT Experiments

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Kiwan Jeon ◽  
Chang-Ock Lee
Keyword(s):  
Electrical Impedance ◽  
Imaging Modality ◽  
Animal Experiments ◽  
Impedance Tomography ◽  
Cross Sectional ◽  
Computational Tools ◽  
Electrically Conducting ◽  
Simulation Validation ◽  
Conductivity Image

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality visualizing static conductivity images of electrically conducting subjects. Recently, MREIT has rapidly progressed in its theory, algorithm, and experiment technique and now reached to the stage ofin vivoanimal experiments. In this paper, we present a software, named CoReHA 2.0 standing for the second version of conductivity reconstructor using harmonic algorithms, to facilitatein vivoMREIT reconstruction of conductivity image. This software offers various computational tools including preprocessing of MREIT data, identification of 2D geometry of the imaging domain and electrode positions, and reconstruction of cross-sectional scaled conductivity images from MREIT data. In particular, in the new version, we added several tools including ramp-preserving denoising, harmonic inpainting, and local harmonicBzalgorithm to deal with data fromin vivoexperiments. The presented software will be useful to researchers in the field of MREIT for simulation, validation, and further technical development.

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