In electrical impedance tomography (EIT), it is difficult to obtain the intracranial impedance due to the highly resistive skull enclosing the brain. Therefore, a high-precision data acquisition system is required for brain EIT. In this paper, we used a high-precision digital synthesis method and a digital demodulation technique with high noise immunity to eliminate random errors. Moreover, we focused on two problems encountered during EIT data acquisition: 1) the shunt effect on the excitation current due to the distributed capacitance between electrodes and ground and 2) high common-mode voltages in the boundary measurements. We designed a new electrode interface to reduce the influence of the distributed capacitance and a programmable current source to accurately compensate for the excited current. We also proposed a new voltmeter circuit with improved CMRR. Overall, this EIT data acquisition system can produce a programmable current with SNR greater than 89 dB. It can also measure the voltage difference precisely with CMRR higher than 75 dB with a 1-kQ impedance imbalance. The results on a calibration model show that this system has a high SNR of 83 dB and a low reciprocity error of 0.125%. In addition, EIT imaging results were acquired using a brain physical phantom. The system can detect small disturbances of 0.35% in volume (1.99% of the cross-sectional area) and 17% in resistivity. Experiments on healthy volunteers also suggest that small intracranial impedance variations due to temporary occlusion and reperfusion of the unilateral carotid artery may be monitored by the system.

In this paper, a voltage multiplier cell is inserted in the conventional boost converter operated in continuous conduction mode (CCM) to provide another design freedom for the voltage-gain extension. Thus, the voltage conversion ratio is enlarged and the narrow turn-OFF period is avoided in the high step-up applications. Furthermore, the voltage multiplier cell makes the voltage stress of all the power devices lower than the high output voltage. As a result, the low-voltage-rated power devices can be employed to reduce the conduction losses and to improve the power device reliability. Moreover, zero-voltage switching soft-switching operation is achieved for the power MOSFETs in the whole-switching transition. Zero-current switching turn-OFF condition is provided for the diodes. Therefore, the power device switching losses and the diode reverse-recovery losses are minimized greatly. In addition, the input current ripple is reduced due to the CCM operation compared with most of the published single-stage single-phase high step-up converters, which makes it more advantageous in the hybrid electric vehicles and the fuel-cell power conversion systems. Finally, the experimental results from a 500-W, 36-380-V prototype are provided to validate the effectiveness of the proposed converter.

Acute stroke is a serious cerebrovascular disease and has been the second leading cause of death worldwide. Conventional diagnostic modalities for stroke, such as CT and MRI, may not be available in emergency settings. Hence, it is imperative to develop a portable tool to diagnose stroke in a timely manner. Since there are differences in impedance spectra between normal, hemorrhagic and ischemic brain tissues, multi-frequency electrical impedance tomography (MFEIT) shows great promise in detecting stroke. Measuring the impedance spectra of healthy, hemorrhagic and ischemic brain in vivo is crucial to the success of MFEIT. To our knowledge, no research has established hemorrhagic and ischemic brain models in the same animal and comprehensively measured the in vivo impedance spectra of healthy, hemorrhagic and ischemic brain within 10 Hz–1 MHz. In this study, the intracerebral hemorrhage and ischemic models were established in rabbits, and then the impedance spectra of healthy, hemorrhagic and ischemic brain were measured in vivo and compared. The results demonstrated that the impedance spectra differed significantly between healthy and stroke-affected brain (i.e., hemorrhagic or ischemic brain). Moreover, the rate of change in brain impedance following hemorrhagic and ischemic stroke with regard to frequency was distinct. These findings further validate the feasibility of using MFEIT to detect stroke and differentiate stroke types, and provide data supporting for future research.

Electrical impedance tomography (EIT) is a promising medical technique for monitoring brain injury through the reflection of electrical impedance changes in internal brain tissues. However, the contact impedances at the electrode-scalp interfaces can still notably affect the EIT accuracy. Hence, we propose a method for real-time monitoring the multi-channel contact impedances of cerebral EIT using a reference electrode attached close to the head vertex. This method allows to approximate inter-channel differences and changes over time of multi-channel contact impedances. The experimental results on 36 volunteers show that the maximum relative inter-channel difference obtained from 16 disposable surface electrodes reaches 18.7%, and the maximum relative change over time reaches 19.2%. The proposed method can assist clinicians in real-time monitoring of contact conditions from the multiple electrode-scalp interfaces during the clinical application of the cerebral EIT.

Electrical impedance tomography (EIT) is an emerging imaging technology that has been used to monitor brain injury and detect acute stroke. The time and frequency properties of electrode–skin contact impedance are important for brain EIT because brain EIT measurement is performed over a long period when used to monitor brain injury, and is carried out across a wide range of frequencies when used to detect stroke. To our knowledge, no study has simultaneously investigated the time and frequency properties of both electrode and conductive gel for brain EIT. In this study, the contact impedance of 16 combinations consisting of 4 kinds of clinical electrode and five types of commonly used conductive gel was measured on ten volunteers’ scalp for a period of 1 h at frequencies from 100 Hz to 1 MHz using the two-electrode method. And then the performance of each combination was systematically evaluated in terms of the magnitude of contact impedance, and changes in contact impedance with time and frequency. Results showed that combination of Ag+/Ag+Cl− powder electrode and low viscosity conductive gel performed best overall (Ten 20® in this study); it had a relatively low magnitude of contact impedance and superior performance regarding contact impedance with time (p < 0.05) and frequency (p < 0.05). Experimental results indicates that the combination of Ag+/Ag+Cl− powder electrode and low viscosity conductive gel may be the best choice for brain EIT.