A non-iterative linear inverse solution for the block approach in EIT

Abbasi, Ataollah; Vahdat, Bijan Vosoughi

doi:10.1016/j.jocs.2010.09.001

Cited by 10 publications

(8 citation statements)

References 26 publications

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“…The problems of cancer diagnosis (see, e.g. [5]) as well as restoring the high-resolution image from the low-level Electrical Impedance Tomography (EIT) images [6] might also be classified as ill-posed.…”

Section: Introductionmentioning

confidence: 99%

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Sawicki

Łoś

Smółka

et al. 2019

Journal of Computational Science

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Section: Introductionmentioning

confidence: 99%

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Sawicki

Łoś

Smółka

et al. 2019

Journal of Computational Science

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“…Abbasi and Vahdat have introduced a non-iterative linear inverse solution for the 2D BM [7]. The remaining sections of this paper are organized as follows: In the next section 2D BM is reviewed in detail.…”

Section: Introductionmentioning

confidence: 99%

Investigation of error propagation and measurement error for 2D block method in Electrical Impedance Tomography

Zaravi

Amirfattahi

Vahdat

2015

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)

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2D block method (2D BM) is a new approach to solve inverse problem as one of the most challenging case in Electrical Impedance Tomography (EIT). In this method, a tissue is modeled by some blocks to construct a medical image of a body limb. Recently, a non-iterative linear inverse solution is introduced to solve the inverse problem in the 2D BM. But effect of measurement error has not been considered for non-iterative linear inverse solution yet. In this paper, an appropriate method is proposed to investigate the error propagation in 2D BM. The effect of measurement error is considered as well through different examples. Results show that the BM is very sensitive to the measurement error and fault propagation depends mainly on the type of tissue conductivity distribution. It also can be show that the error increases exponentially in each calculation step. Keywords: Electrical Impedance Tomography (EIT), error propagation, measurement error, 2D block method978-1-4799-8445-9/15/$31.00 ©2015 IEEE

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“…The problem of high computational burden faced in this method can be resolved by the method of modified equations [18]. Recently, a non-iterative linear inverse solution is introduced in [19] that raises the efficiency of this method via reduction in its computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Precise two-dimensional D-bar reconstructions of human chest and phantom tank via sinc-convolution algorithm

Abbasi

Naghsh-Nilchi

2012

BioMedical Engineering OnLine

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BackgroundElectrical Impedance Tomography (EIT) is used as a fast clinical imaging technique for monitoring the health of the human organs such as lungs, heart, brain and breast. Each practical EIT reconstruction algorithm should be efficient enough in terms of convergence rate, and accuracy. The main objective of this study is to investigate the feasibility of precise empirical conductivity imaging using a sinc-convolution algorithm in D-bar framework.MethodsAt the first step, synthetic and experimental data were used to compute an intermediate object named scattering transform. Next, this object was used in a two-dimensional integral equation which was precisely and rapidly solved via sinc-convolution algorithm to find the square root of the conductivity for each pixel of image. For the purpose of comparison, multigrid and NOSER algorithms were implemented under a similar setting. Quality of reconstructions of synthetic models was tested against GREIT approved quality measures. To validate the simulation results, reconstructions of a phantom chest and a human lung were used.ResultsEvaluation of synthetic reconstructions shows that the quality of sinc-convolution reconstructions is considerably better than that of each of its competitors in terms of amplitude response, position error, ringing, resolution and shape-deformation. In addition, the results confirm near-exponential and linear convergence rates for sinc-convolution and multigrid, respectively. Moreover, the least degree of relative errors and the most degree of truth were found in sinc-convolution reconstructions from experimental phantom data. Reconstructions of clinical lung data show that the related physiological effect is well recovered by sinc-convolution algorithm.ConclusionsParametric evaluation demonstrates the efficiency of sinc-convolution to reconstruct accurate conductivity images from experimental data. Excellent results in phantom and clinical reconstructions using sinc-convolution support parametric assessment results and suggest the sinc-convolution to be used for precise clinical EIT applications.

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A non-iterative linear inverse solution for the block approach in EIT

Cited by 10 publications

References 26 publications

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Investigation of error propagation and measurement error for 2D block method in Electrical Impedance Tomography

Precise two-dimensional D-bar reconstructions of human chest and phantom tank via sinc-convolution algorithm

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