Comparison of statistical inversion with iteratively regularized Gauss Newton method for image reconstruction in electrical impedance tomography

Ahmad, Sanwar Uddin; Strauss, Thilo; Kupis, Shyla; Khan, Taufiquar

doi:10.1016/j.amc.2019.03.063

Cited by 26 publications

(19 citation statements)

References 34 publications

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“…The relationship between the change of total conduction impedance and the change of single element impedance is established should try to reduce the impact of the staircase effect to obtain higher resolution imaging results. The subsequent research on regularization algorithm is mostly based on the two research, Borsic et al (2009) tried to solve the time division inverse problem of TV regularization by using the primal dual interior point method; Yoon et al (2014) proposed a first-order TV regularization algorithm-linear alternative direction multiplier algorithm, which was used to solve the EIT inverse problem; Liu et al (2013) also combined the total variation regularization algorithm with the Tikhonov regularization algorithm to obtain better reconstructed images; Lysaker et al (2003) proposed using the second-order differential to construct the regular LLT (Low-Latency Trend line) model and obtained better practical results, but it is easy to produce fuzzy phenomenon in the image edge; in 2019, Shi et al (2019) proposed a regularized ladder effect suppression algorithm based on generalized variation, which can meet the needs of high resolution, suppress the ladder effect, and improve accuracy; Ahmad et al (2019) used statistical inversion method based on Bayes theorem and an iterative regularized Gauss-Newton method for image reconstruction of EIT; Wang (2020) proposed a non-convex p-norm sparsity-promoting regularization. Compared with L-1 norm regularization, it improves the spatial resolution and is more robust to noise.…”

Section: It Can Eliminate Noise Interference and Its Own Errormentioning

confidence: 99%

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

Shi

Yang

Xie

et al. 2021

Front. Bioeng. Biotechnol.

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Medical imaging can intuitively show people the internal structure, morphological information, and organ functions of the organism, which is one of the most important inspection methods in clinical medical diagnosis. Currently used medical imaging methods can only be applied to some diagnostic occasions after qualitative lesions have been generated, and the general imaging technology is usually accompanied by radiation and other conditions. However, electrical impedance tomography has the advantages of being noninvasive and non-radiative. EIT (Electrical Impedance Tomography) is also widely used in the early diagnosis and treatment of some diseases because of these advantages. At present, EIT is relatively mature and more and more image reconstruction algorithms are used to improve imaging resolution. Hardware technology is also developing rapidly, and the accuracy of data collection and processing is continuously improving. In terms of clinical application, EIT has also been used for pathological treatment of lungs, the brain, and the bladder. In the future, EIT has a good application prospect in the medical field, which can meet the needs of real-time, long-term monitoring and early diagnosis. Aiming at the application of EIT in the treatment of lung pathology, this article reviews the research progress of EIT, image reconstruction algorithms, hardware system design, and clinical applications used in the treatment of lung diseases. Through the research and introduction of several core components of EIT technology, it clarifies the characteristics of EIT system complexity and its solutions, provides research ideas for subsequent research, and once again verifies the broad development prospects of EIT technology in the future.

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Section: It Can Eliminate Noise Interference and Its Own Errormentioning

confidence: 99%

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

Shi

Yang

Xie

et al. 2021

Front. Bioeng. Biotechnol.

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“…The Gauss-Newton algorithm was used for image reconstruction and the total variation approach was used for regularization. These algorithms are available in the Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) [26,27]. Because the EIT system has 8 electrodes and because voltages at the injection electrode and at its two nearest neighbors are not measured, N = 40, corresponding with 8 current injections and 5 voltage measurements for each frame.…”

Section: Global Impedancementioning

confidence: 99%

Robustness of focused and global impedance estimates of bladder volumes against uncertainty of urine conductivity

Leyton

Bardia

Rodas

2020

Biomed. Phys. Eng. Express

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Bioimpedance measurements are currently used to monitor various biological processes and are potentially useful for studies of urodynamics. Global impedance (GI) and focused impedance measurements (FIM) can be used to monitor bladder volumes, but these are subject to varying conductivity of urine. To address this, we emulated a human bladder using an agar phantom filled with saline solutions of varying conductivities and estimated volumes using a modified FIM-based approach. Using this novel strategy, electrical potentials did not change significantly with constant liquid volumes, even when the conductivity of the saline solutions was varied between 1.027 to 1.877 and 2.610 S/m. Conversely, GI and classic FIM measurements of constant liquid volumes varied with conductivity. These observations suggest that the proposed FIM approach is suitable for bladder volume estimation due to its robustness against uncertainties of conductivity. The bioimpedance hardware used in our experiments comprised 8 electrodes and a a small and low cost impedance measurement system based on an AFE4300 direct impedance measurement device.

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“…As such, ill-posed problems cannot be directly inverted without the introduction of a priori information or regularization to the model in order to attenuate the ill-posedness, as commonly done during the resolution of the Inverse Problem. In fact, several methods have been proposed in literature to tackle the non-linear behavior and ill-posed nature of EIT in the form of analytical approaches such as the back-projection algorithm 16 , global impedance 17 or variants to the traditional Gauss-Newton method, including single-step 7 and multi-step 18 reconstructions, the latter with iterative estimation of the conductivity map by re-calculating the Jacobian and Hessian matrices derived from the Taylor's series expansion of the EIT functional at every step. To cope with the computationally demanding calculation of the previous matrices, numerical approximations are often pursued by the sensitivity approach 19 , especially in large 3D domains, whereas the incorporation of a regularization term helps to stabilize the inverse process itself.…”

Section: Introductionmentioning

confidence: 99%

Non-linear Optimization Scheme Applied to EIT Medical Images Based on the Gauss-Newton Method With Iterative Conductivity Update and Total Variation Regularization

Rosa

Yang

2021

Preprint

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Medical imaging modalities are vast and include the well-established techniques of Computerized Tomography (CT), Magnetic Resonance Imaging (MRI) or Ultrasounds. Though the underlying physics differs amongst modalities, a similar Inverse Mapping is shared: the recovery of the internal physical property under analysis from measurements obtained at the exterior of the body. In the lesser common technique of Electrical Impedance Tomography (EIT), measurement of the voltage levels around a body part are used to retrieve the internal distribution map for conductivity. The complexity of the Inverse Problem in EIT surpasses that found for the Forward Problem (or Mapping), both in terms of mathematical formulation and computational overload. The ill-posed nature of the Inverse Problem further contributes to that intricacy, and solutions to tackle it will be presented in this paper by means of a non-linear optimization scheme involving the iterative Gauss-Newton (GN) method with Total Variation (TV) regularization, applied to two-dimensional (2D) body domains only. The proposed scheme is also compared to other traditional reconstruction algorithms like 2D back-projection and the sensitivity approximation to the Jacobian matrix of the system of equations governing EIT. Results from conductivity map reconstructions in anatomical phantoms have shown an improvement in signal-to-noise ratio (SNR) and distribution map error (DME) of 36% and 11% for the proposed non-linear method relative to the back-projection and single-step GN method with sensitivity approximation.

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Comparison of statistical inversion with iteratively regularized Gauss Newton method for image reconstruction in electrical impedance tomography

Cited by 26 publications

References 34 publications

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

Robustness of focused and global impedance estimates of bladder volumes against uncertainty of urine conductivity

Non-linear Optimization Scheme Applied to EIT Medical Images Based on the Gauss-Newton Method With Iterative Conductivity Update and Total Variation Regularization

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