A novel combined regularization algorithm of total variation and Tikhonov regularization for open electrical impedance tomography

Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Objective.Wearable electrical impedance tomography (EIT) can be used to monitor regional lung ventilation and perfusion at the bedside. Due to its special system architecture, the amplitude of the injected current is usually limited compared to stationary EIT system. This study aims to evaluate the performance of current injection patterns with various low-amplitude currents in healthy volunteers.Approach.A total of 96 test sets of EIT measurement was recorded in 12 healthy subjects by employing adjacent and opposite current injection patterns with four amplitudes of small current (i.e. 1 mA, 500 uA, 250 uA and 125 uA). The performance of the two injection patterns with various currents was evaluated in terms of signal-to-noise ratio (SNR) of thorax impedance, EIT image metrics and EIT-based clinical parameters.Main results.Compared with adjacent injection, opposite injection had higher SNR (p< 0.01), less inverse artifacts (p< 0.01), and less boundary artifacts (p< 0.01) with the same current amplitude. In addition, opposite injection exhibited more stable EIT-based clinical parameters (p< 0.01) across the current range. For adjacent injection, significant differences were found for three EIT image metrics (p< 0.05) and four EIT-based clinical parameters (p< 0.01) between the group of 125 uA and the other groups.Significance.For better performance of wearable pulmonary EIT, currents greater than 250 uA should be used in opposite injection, 500 uA in adjacent one, to ensure a high level of SNR, a high quality of reconstructed image as well as a high reliability of clinical parameters.

Imaging of Magnetic Nanoparticles With Permeability Tomography

Magnetic nanoparticles are being increasingly used in numerous biomedical applications for diagnosis and therapy. During the course of these applications nanoparticle biodegradation and body clearance may occur. In this context, a portable, non-invasive, non-destructive and contactless imaging device can be relevant to trace the nanoparticle distribution before and after the medical procedure. We present a method for in vivo imaging the nanoparticles based on the magnetic induction technique, and we show how to properly tune it for magnetic permeability tomography, maximizing the permeability selectivity. A tomograph prototype was designed and built to demonstrate the feasibility of the proposed method. It includes data collection, signal processing and image reconstruction. Useful selectivity and resolution are achieved on phantoms and animals, proving that the device can be used to monitor the presence of magnetic nanoparticles without requiring any particular sample preparation. By this way, we show that magnetic permeability tomography may become a powerful technique to assist medical procedures.

A new current injection and voltage measurement strategy of 3D electrical impedance tomography based on scanning electrode

Electrical impedance tomography (EIT) technology is an important imaging approach to show the conductivity distribution of the area noninvasively. Recently, 3D EIT has been extensively studied for its more comprehensive display of electrical properties. Nonetheless, most 3D EIT electrode models are based on multilayer ring electrodes and only suitable for specific scenarios. In order to overcome its limitations and alleviate the ill-condition of 3D EIT, we propose a new current injection and voltage measurement strategy based on scanning row electrodes (SRE) called the back electrode excitation (BEEM) strategy and select the optimal number of excitation electrodes according to different imaging effects. A 3D electrical impedance imaging system based on SRE is designed. Then, the traditional excitation measurement strategy is introduced, and the two strategies are compared through simulation and actual experiments. The results show that the BEEM strategy with SRE can not only obtain rich potential information in the finite field but also significantly improve the imaging detection depth, accuracy, and noise immunity compared with the flat electrode array.

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

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.

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

General regularization framework for DEER spectroscopy

Tikhonov regularization is the standard processing technique for the inversion of double electron-electron resonance (DEER) data to distance distributions without assuming a parametrized model. In other fields it has been surpassed by modern regularization methods. We analyze such alternative regularization methods based on the Tikhonov, total variation (TV) and Huber penalties with and without the use of Bregman iterations. For this, we provide a general mathematical framework and its open-source software implementation. We extend an earlier approach by Edwards and Stoll for the selection of an optimal regularization parameter to all of these penalties and use their big test data set of noisy DEER traces with known ground truth for assessment. The results indicate that regularization methods based on Bregman iterations provide an improvement upon Tikhonov regularization in recognizing features and recovering distribution width at moderate signal-to-noise ratio, provided that noise variance is known. Bregman-iterative methods are robust with respect to the method used in the choice of regularization parameter.

Electrical impedance tomography imaging of the cardiopulmonary system

PURPOSE OF REVIEW

This review article summarizes the recent advances in electrical impedance tomography (EIT) related to cardiopulmonary imaging and monitoring on the background of the 30-year development of this technology.

RECENT FINDINGS

EIT is expected to become a bedside tool for monitoring and guiding ventilator therapy. In this context, several studies applied EIT to determine spatial ventilation distribution during different ventilation modes and settings. EIT was increasingly combined with other signals, such as airway pressure, enabling the assessment of regional respiratory system mechanics. EIT was for the first time used prospectively to define ventilator settings in an experimental and a clinical study. Increased neonatal and paediatric use of EIT was noted. Only few studies focused on cardiac function and lung perfusion. Advanced radiological imaging techniques were applied to assess EIT performance in detecting regional lung ventilation. New approaches to improve the quality of thoracic EIT images were proposed.

SUMMARY

EIT is not routinely used in a clinical setting, but the interest in EIT is evident. The major task for EIT research is to provide the clinicians with guidelines how to conduct, analyse and interpret EIT examinations and combine them with other medical techniques so as to meaningfully impact the clinical decision-making.

Linearly constrained minimum variance spatial filtering for localization of conductivity changes in electrical impedance tomography

We localize dynamic electrical conductivity changes and reconstruct their time evolution introducing the spatial filtering technique to electrical impedance tomography (EIT). More precisely, we use the unit-noise-gain constrained variation of the distortionless-response linearly constrained minimum variance spatial filter. We address the effects of interference and the use of zero gain constraints. The approach is successfully tested in simulated and real tank phantoms. We compute the position error and resolution to compare the localization performance of the proposed method with the one-step Gauss-Newton reconstruction with Laplacian prior. We also study the effects of sensor position errors. Our results show that EIT spatial filtering is useful for localizing conductivity changes of relatively small size and for estimating their time-courses. Some potential dynamic EIT applications such as acute ischemic stroke detection and neuronal activity localization may benefit from the higher resolution of spatial filters as compared to conventional tomographic reconstruction algorithms.

Modified Landweber algorithm for robust particle sizing by using Fraunhofer diffraction

In this paper, a robust modified Landweber algorithm was proposed to retrieve the particle size distributions from Fraunhofer diffraction. Three typical particle size distributions, i.e., Rosin-Rammler, lognormal, and bimodal normal distributions for particles ranging from 4.8 to 96 μm, were employed to verify the performance of the algorithm. To show its merits, the proposed algorithm was compared with the Tikhonov regularization algorithm and the ℓ1-norm-based algorithm. Simulation results showed that, for noise-free data, both the modified Landweber algorithm and the ℓ1-norm-based algorithm were better than the Tikhonov regularization algorithm in terms of accuracy. When the data was noise-contaminated, the modified Landweber algorithm was superior to the other two algorithms in both accuracy and speed. An experimental setup was also established and the results validated the feasibility and effectiveness of the proposed method.

The differential Howland current source with high signal to noise ratio for bioimpedance measurement system

The stability and signal to noise ratio (SNR) of the current source circuit are the important factors contributing to enhance the accuracy and sensitivity in bioimpedance measurement system. In this paper we propose a new differential Howland topology current source and evaluate its output characters by simulation and actual measurement. The results include (1) the output current and impedance in high frequencies are stabilized after compensation methods. And the stability of output current in the differential current source circuit (DCSC) is 0.2%. (2) The output impedance of two current circuits below the frequency of 200 KHz is above 1 MΩ, and below 1 MHz the output impedance can arrive to 200 KΩ. Then in total the output impedance of the DCSC is higher than that of the Howland current source circuit (HCSC). (3) The SNR of the DCSC are 85.64 dB and 65 dB in the simulation and actual measurement with 10 KHz, which illustrates that the DCSC effectively eliminates the common mode interference. (4) The maximum load in the DCSC is twice as much as that of the HCSC. Lastly a two-dimensional phantom electrical impedance tomography is well reconstructed with the proposed HCSC. Therefore, the measured performance shows that the DCSC can significantly improve the output impedance, the stability, the maximum load, and the SNR of the measurement system.