Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

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The High-frequency second-order sensitivity matrix electrical impedance tomography (HSSM-EIT) method has been proposed to detect heterogeneous cells in cell spheroids by coupling the high-frequency and second-order sensitivity matrix EIT. The sensitivity matrix with the first and second-order terms of Taylor's formula (Jacobian and Hessian) is applied to the image reconstruction of cell spheroids with the high-frequency injected current at 1 MHz, at which the impedance reflects intracellular contents to visualize the cytoplasm conductivity distribution of cell spheroids. The cell spheroids with five compositions percentages of wild type (WT) and green fluorescent protein type (GFPT) of MRC-5 human lung fibroblast cell line are 100/0%, 75/25%, 50/50%, 25/75%, and 0/100%, were cultured to mimic heterogeneous cells. As a result, the cell spheroid images reconstructed by HSSM-EIT clearly visualize the heterogeneity stage rather than the images reconstructed by general first-order sensitivity matrix EIT; moreover, the cytoplasm conductivity of cell spheroid is decreased with the increase of GFPT percentage. In order to confirm the cytoplasm conductivity reconstructed by HSSM-EIT, an equivalent circuit model containing a cell spheroid and extracellular fluid is employed to calculate the cytoplasm conductivity σcyto from the measurement of electrochemical impedance spectroscopy. The result shows that σcyto is also decreased with the increase of GFPT percentage, which shows the same trend as the cytoplasm conductivity reconstructed by HSSM-EIT.

The image reconstruction in electrical impedance tomography (EIT) has low accuracy due to the approximation error between the measured voltage change and the approximated voltage change, from which the object cannot be accurately reconstructed and quantitatively evaluated. A voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) is proposed to reconstruct the image with high accuracy. In the OO-SME model, a sensitivity matrix of the object-field is estimated, and the sensitivity matrix change from the background-field to the object-field is estimated to optimize the approximated voltage change, from which the approximation error is eliminated to improve the reconstruction accuracy. Against the existing linear and nonlinear models, the approximation error in the OO-SME model is eliminated, thus an image with higher accuracy is reconstructed. The simulation shows that the OO-SME model reconstructs a more accurate image than the existing models for quantitative evaluation. The relative accuracy (RA) of reconstructed conductivity is increased up to 83.98% on average. The experiment of lean meat mass evaluation shows that the RA of lean meat mass is increased from 7.70% with the linear model to 54.60% with the OO-SME model. It is concluded that the OO-SME model reconstructs a more accurate image to evaluate the object quantitatively than the existing models.