The design and performance of the active complex electrode (ACE1) electrical impedance tomography system for single-ended phasic voltage measurements are presented. The design of the hardware and calibration procedures allows for reconstruction of conductivity and permittivity images. Phase measurement is achieved with the ACE1 active electrode circuit which measures the amplitude and phase of the voltage and the applied current at the location at which current is injected into the body. An evaluation of the system performance under typical operating conditions includes details of demodulation and calibration and an in-depth look at insightful metrics, such as signal-to-noise ratio variations during a single current pattern. Static and dynamic images of conductivity and permittivity are presented from ACE1 data collected on tank phantoms and human subjects to illustrate the system's utility.

Objective : Absolute images have important applications in medical electrical impedance tomography (EIT) imaging, but the traditional minimization and statistical based computations are very sensitive to modeling errors and noise. In this paper, it is demonstrated that D-bar reconstruction methods for absolute EIT are robust to such errors. Approach : The effects of errors in domain shape and electrode placement on absolute images computed with 2D D-bar reconstruction algorithms are studied on experimental data. Main results : It is demonstrated with tank data from several EIT systems that these methods are quite robust to such modeling errors, and furthermore the artefacts arising from such modeling errors are similar to those occurring in classic time-difference EIT imaging. Significance : This study is promising for clinical applications where absolute EIT images are desirable but previously thought impossible.

Electrical impedance tomography (EIT) is a non-invasive medical imaging technique in which images of the conductivity in a region of interest in the body are computed from measurements of voltages on electrodes arising from low-frequency, low-amplitude applied currents. Mathematically, the inverse conductivity problem is nonlinear and ill-posed, and the reconstructions have characteristically low spatial resolution. One approach to improve the spatial resolution of EIT images is to include anatomically and physiologically-based prior information in the reconstruction algorithm. Statistical inversion theory provides a means of including prior information from a representative sample population. In this paper, a method is proposed to introduce statistical prior information into the D-bar method based on Schur complement properties. The method presents an improvement of the image obtained by the D-bar method by maximizing the conditional probability density function of an image that is consistent with a prior information and the model, given a D-bar image computed from the voltage measurements. Experimental phantoms show an improved spatial resolution by the use of the proposed method for the D-bar image reconstructions.

In previous work by Rueter et al., low frequency (10–750 kHz) ultrasound was shown to penetrate the lung, motivating its use as a nonionizing tomographic technique for pulmonary monitoring. Here, we present a method for regularized full waveform inversion for low-frequency tomographic ultrasound data. A novel structural similarity index metric (SSIM)-based regularization term is introduced that compares the structural correlation of the current sound speed iterate to a prior reconstruction computed by electrical impedance tomography (EIT). Full waveform reconstructions of sound speeds from numerically simulated low frequency ultrasound data with 0.1% additive Gaussian noise are computed, and the results are compared using Tikhonov regularization, total variation regularization, and both terms combined with the SSIM-EIT regularization term. The EIT reconstruction was computed from simulated voltage data on the same phantom using one step of a Newton-Raphson method with a high-pass Gaussian filter as a regularizer. Reconstructions including the SSIM-EIT regularization term converged fastest, and an adaptive regularization parameter provided the most accurate reconstructions. The method is then iterated by computing an EIT reconstruction using the USCT result as a prior, and a subsequent USCT reconstruction is computed using the SSIM-EIT regularization term with the updated EIT reconstruction.