The rapid printing of 3D parts with desired electrical properties enables numerous applications. Fused deposition modeling (FDM) using conductive thermoplastic composites has been a valuable approach for such fabrication. The parts produced by FDM possess various controllable structural features, but the effects of the structural features on the electrical properties remain to be determined. This study investigated the effects of these features on the electrical resistivity and resistivity anisotropy of 3D-printed ABS/CB composites. The effects of the process parameters of FDM, including the layer thickness, raster width, and air gap, on the resistivity in both the vertical and horizontal directions for cubic samples were studied because the internal structure of the printed parts depended on those process parameters. The resistivities of printed parts in different parameter combinations were measured by an impedance analyzer and finite element models were created to investigate the relationship between the resistivity and the internal structure. The results indicated that the parameters remarkably affected the resistivity due to the influence of voids and the bonding condition between adjacent fibers. The resistivity in the vertical direction ranged from 70.40 ± 2.88 Ω·m to 180.33 ± 8.21 Ω·m, and the resistivity in the horizontal direction ranged from 41.91 ± 2.29 Ω·m to 58.35 ± 0.61 Ω·m at the frequency of 1 kHz. Moreover, by adjusting the resistivities in different directions, the resistivity anisotropy of the printed parts can be manipulated from 1.01 to 3.59. This research may serve as a reference to fabricate parts with sophisticated geometry with desired electrical resistivity and resistivity anisotropy.

Phantom experiments are an important step for testing during the development of new hardware or imaging algorithms for head electrical impedance tomography (EIT) studies. However, due to the sophisticated anatomical geometry and complex resistivity distribution of the human head, constructing an accurate phantom for EIT research remains challenging, especially for skull modelling. In this paper, we designed and fabricated a novel head phantom with anatomically realistic geometry and continuously varying skull resistivity distribution based on 3D printing techniques. The skull model was constructed by simultaneously printing the distinct layers inside the skull with resistivity-controllable printing materials. The entire phantom was composed of saline skin, a 3D-printed skull, saline cerebrospinal fluid (CSF) and 3D-printed brain parenchyma. The validation results demonstrated that the resistivity of the phantom was in good agreement with that of human tissue and was stable over time, and the new phantom performed well in EIT imaging. This paper provides a standardized, efficient and reproducible method for the construction of a head phantom for EIT that could be easily adapted to other conditions for manufacturing head phantoms for brain function research, such as transcranial direct current stimulation (TDCS) and electroencephalography (EEG).

Stroke is a severe cerebrovascular disease and is the second greatest cause of death worldwide. Because diagnostic tools (CT and MRI) to detect acute stroke cannot be used until the patient reaches the hospital setting, a portable diagnostic tool is urgently needed. Because biological tissues have different impedance spectra under normal physiological conditions and different pathological states, multi-frequency electrical impedance tomography (MFEIT) can potentially detect stroke. Accurate impedance spectra of normal brain tissue (gray and white matter) and stroke lesions (ischemic and hemorrhagic tissue) are important elements when studying stroke detection with MFEIT. To our knowledge, no study has comprehensively measured the impedance spectra of normal brain tissue and stroke lesions for the whole frequency range of 1 MHz within as short as possible an ex vivo time and using the same animal model. In this study, we established intracerebral hemorrhage and ischemic models in rabbits, then measured and analyzed the impedance spectra of normal brain tissue and stroke lesions ex vivo within 15 min after animal death at 10 Hz to 1 MHz. The results showed that the impedance spectra of stroke lesions significantly differed from those of normal brain tissue; the ratio of change in impedance of ischemic and hemorrhagic tissue with regard to frequency was distinct; and tissue type could be discriminated according to its impedance spectra. These findings further confirm the feasibility of detecting stroke with MFEIT and provide data supporting further study of MFEIT to detect stroke.

Objective : The aim of the study was to examine the pros and cons of different types of functional EIT (fEIT) to quantify tidal ventilation distribution in a clinical setting. Approach : fEIT images were calculated with (1) standard deviation of pixel time curve, (2) regression coefficients of global and local impedance time curves, or (3) mean tidal variations. To characterize temporal heterogeneity of tidal ventilation distribution, another fEIT image of pixel inspiration times is also proposed. Main results : fEIT—regression is very robust to signals with different phase information. When the respiratory signal should be distinguished from the heart-beat related signal, or during high-frequency oscillatory ventilation, fEIT—regression is superior to other types. fEIT—tidal variation is the most stable image type regarding the baseline shift. We recommend using this type of fEIT image for preliminary evaluation of the acquired EIT data. However, all these fEITs would be misleading in their assessment of ventilation distribution in the presence of temporal heterogeneity. Significance : The analysis software provided by the currently available commercial EIT equipment only offers either fEIT of standard deviation or tidal variation. Considering the pros and cons of each fEIT type, we recommend embedding more types into the analysis software to allow the physicians dealing with more complex clinical applications with on-line EIT measurements.

Individualized lung-protective ventilation strategy is essential for patients with COVID-19. In this study, a wireless, low-power, and miniaturized electrical impedance tomography (EIT) system was developed for remote and long-term monitoring of lung ventilation for patients with COVID-19 in the isolation ward of ICU. A new strategy of combining filtering and improved voltage-controlled current was employed to design the current source for a simplified system structure, and a new differential receiver circuit consisting of a buffer amplification and a differential amplification was proposed to design the voltage measurement unit for high precision. Moreover, a Bluetooth interface was adopted for wireless data transmission, and components characterized by low power consumption were selected to minimize the system power consumption. The proposed EIT system occupies a total size of $6.5\times 4.5\times0.6$ cm ³ and can work stably within 25 m from the wireless terminal installed with host EIT software. The system has a signal-to-noise of 70 dB at 50 kHz, stability of 0.1% relative change, and power consumption of 114 mW, as tested with a resistor phantom. Furthermore, a comparison between the proposed system and a commercial ICU EIT device was conducted on nine healthy volunteers for ventilation monitoring. The correlation between lung volume and relative impedance changes was higher than 0.9 for both EIT systems ( $p < 0.001$ ). The analysis of the corresponding EIT images revealed that both systems delivered comparable images in terms of linearity, repeatability, and regional ventilation distribution. The test and experimental results suggested that the proposed system could conveniently provide reliable data acquisition (DAQ) for remote and long-term lung ventilation monitoring.

Electrical impedance tomography (EIT) is a noninvasive, radiation-free, and low-cost imaging modality for monitoring the conductivity distribution inside a patient. Nowadays, time-difference EIT (tdEIT) is used extensively as it has fast imaging speed and can reflect the dynamic changes of diseases, which make it attractive for a number of medical applications. Moreover, modeling errors are compensated to some extent by subtraction of voltage measurements collected before and after the change. However, tissue conductivity varies with frequency and tdEIT does not efficiently exploit multi-frequency information as it only uses measurements associated with a single frequency. This paper proposes a tdEIT algorithm that imposes spectral constraints on the framework of the linear least squares problem. Simulation and phantom experiments are conducted to compare the proposed spectral constraints algorithm (SC) with the damped least squares algorithm (DLS), which is a stable tdEIT algorithm used in clinical practice. The condition number and rank of the matrices needing inverses are analyzed, and image quality is evaluated using four indexes. The possibility of multi-tissue imaging and the influence of spectral errors are also explored. Significant performance improvement is achieved by combining multi-frequency and time-difference information. The simulation results show that, in one-step iteration, both algorithms have the same condition number and rank, but SC effectively reduces image noise by 20.25% compared to DLS. In addition, deformation error and position error are reduced by 8.37% and 7.86%, respectively. In two-step iteration, the rank of SC is greatly increased, which suggests that more information is employed in image reconstruction. Image noise is further reduced by an average of 32.58%, and deformation error and position error are also reduced by 20.20% and 31.36%, respectively. The phantom results also indicate that SC has stronger noise suppression and target identification abilities, and this advantage is more obvious with iteration. The results of multi-tissue imaging show that SC has the unique advantage of automatically extracting a single tissue to image. SC enables tdEIT to utilize multi-frequency information in cases where the spectral constraints are known and then provides higher quality images for applications.