A digital system for bioimpedance and electrical impedance tomography (EIT) measurement controlled by an ATmega16 microcontroller was constructed in our laboratory. There are eight digital electrodes using AD5933 to measure the impedance of the targets, and the data is transmitted to the computer wirelessly through nRF24L01. The structure of the system, circuit design, system testing, vitro measurements of animals' tissues and electrical impedance tomography are introduced specifically in this paper. The experimental results showed that the system relative error was 0.42%, and the signal noise ratio was 76.3 dB. The system not only can be used to measure the impedance by any two electrodes within the frequency of 1-100 kHz in a sweep scanning, but also can reconstruct the images of EIT. The animal experiments showed that the data was valid and plots were fitting with Cole-Cole theory. The testing verified the feasibility and effectiveness of the system. The images reconstructed of a salt-water tank are satisfactory and match with the actual distribution of the tank. The system improves the effectiveness of the front-end measuring signal and the stability of the system greatly.
In recent years, the technologies based on the electrical properties (EPs) of human tissue, such as electrical impedance tomography (EIT) and magnetic resonance electrical impedance tomography (MREIT), have become one of the most popular research subjects in biomedicine. Compared with EIT and MREIT, the magnetic resonance electrical property tomography (MR-EPT) is a new technique using nondestructive EPs method. MR-EPT reconstructs the electrical conductivity and permittivity of the biological tissues based on the radio frequency field of the magnetic resonance imaging (MRI) system. It can obtain an accurate and high resolution image without current injection. In this paper, several methods for the EPs are reviewed, especially the MR-EPT. The theory, advantages and prospects of MR-EPT's are elaborated. The method of specific absorption rate (SAR) based on it is also introduced. MR-EPT is deserved further research and should be given more attention by the researchers. All this evolution based on MREPT can give new energy to the medical diagnosis.
In order to explore the feasibility of applying magnetic detection electrical impedance tomography (MDEIT) on respiratory monitoring, aiming at the forward problem of magnetic detection electrical impedance tomography, we calculated the electric potential and current density distribution inside the imaging object by using the finite element method. We then got magnetic induction intensity outside the object at the end of exhaling and inhaling according to Biot-Savart's law. The results showed that the magnetic induction intensity at the end of inhaling was 8.875%, less than that at the end of exhaling. By the simulation results, we could understand the difference of magnetic induction intensity value surrounding the lung at the end of exhaling and inhaling due to the change of lung volume and electrical conductivity distribution better. Our research laid the foundation for the late image reconstruction and clinical disease detection.
The inverse problem of electrical impedance tomography (EIT) is seriously ill-posed, which restricts the clinical application of EIT. Regularization is an important numerical method to improve the stability of the EIT inverse problem as well as the resolution of the imaging. This paper proposes a self-diagnosis regularization method based on Tikhonov regularization and diagonal weight regularization method (DWRM). Firstly, the ill-posedness of the inverse problem is analyzed by sensitivity. Then, the performance of the self-diagnosis regularization is analyzed through the singular value theory. Finally, some simulated experiments including simulations and flume experiment are carried out and verify that the self-diagnosis regularization has better image quality and anti-noise ability than those of traditional regularization methods. The self-diagnosis regularization method weakens the ill-posedness of inverse problem of EIT and can prompt the practical application of EIT.
This study aims to propose a multifrequency time-difference algorithm using spectral constraints. Based on the knowledge of tissue spectrum in the imaging domain, the fraction model was used in conjunction with the finite element method (FEM) to approximate a conductivity distribution. Then a frequency independent parameter (volume or area fraction change) was reconstructed which made it possible to simultaneously employ multifrequency time-difference boundary voltage data and then reduce the degrees of freedom of the reconstruction problem. Furthermore, this will alleviate the illness of the EIT inverse problem and lead to a better reconstruction result. The numerical validation results suggested that the proposed time-difference fraction reconstruction algorithm behaved better than traditional damped least squares algorithm (DLS) especially in the noise suppression capability. Moreover, under the condition of low signal-to-noise ratio, the proposed algorithm had a more obvious advantage in reconstructions of targets shape and position. This algorithm provides an efficient way to simultaneously utilize multifrequency measurement data for time-difference EIT, and leads to a more accurate reconstruction result. It may show us a new direction for the development of time-difference EIT algorithms in the case that the tissue spectrums are known.
Objective To explore the clinical application value of electrical impedance tomography (EIT) individualized adjustment of positive end-expiratory pressure (PEEP) in patients with acute respiratory distress syndrome (ARDS). Methods The ARDS patients requiring mechanical ventilation who admitted between April 2019 and March 2022 were recruited in the study. They were randomly divided into 3 groups with 12 cases in each group. Optimal PEEP was set using ARDSnet method (a control group), lung ultrasound scoring method (LUS group) and EIT adjustment method (EIT group). The changes of hemodynamics, blood gas analysis, respiratory mechanics, extravascular lung water index and other indicators of the patients were recorded at each time point. Results There was no significant difference in PEEP between the EIT group and the LUS group, but PEEP in both the EIT group and the LUS group was significantly higher than the control group (P<0.05). After 12 hours of treatment, the dynamic lung compliance of the control group did not change significantly, while the dynamic lung compliance ventilation of the LUS group and the EIT group was significantly improved for 12 hours, and the improvement in the EIT group was significantly better than that in the control group (P<0.05). After treatment, the oxygenation index in the three groups was significantly increased, and the oxygenation index in the EIT group was significantly higher than that in the control group (P<0.05). There was no significant difference in hemodynamics between the three groups before and after treatment (P>0.05). The extravascular lung water index of the three groups after treatment was significantly decreased, and the LUS group and the EIT group decreased more significantly than the control group (P<0.05). Conclusion In the PEEP setting of ARDS patients, the use of EIT personalized adjustment method can effectively improve the patient’s lung compliance and oxygenation index, and reduce extravascular lung water, without affecting hemodynamics.