As a nondestructive, nonradioactive, and high temporal resolution technique, electrical impedance tomography (EIT) has potential applications in industrial and biomedical imaging. A novel geometrically constrained boundary reconstructor (GCBR) is presented for EIT. It can directly calculate the boundary of the target inclusions embedded into a homogeneous background conductivity and is robust to noise. The proposed GCBR depends on a specially designed energy function, which consists of the residual term and the geometric constraints' term. The residual term drives the estimated inclusion boundary to the targets. The geometric constraints' term regularizes the estimations and automatically excludes the meaningless boundary guesses from the candidate solution. A set of numerical tests are conducted to discuss the key factors influencing the boundary reconstruction performance. A set of experimental tests are conducted to evaluate the performance of the proposed GCBR. The position, size, and shape of the target inclusions are well reconstructed from the real data. The mean position error and mean Hausdorff distances between the target and reconstructed boundaries are 0.39% and 2.2% of the maximal observation diameter, respectively. The mean area errors and the mean area of the symmetric difference between the target and reconstructed inclusion regions are 0.32% and 2.54% of the total observation area, respectively.

As a non-invasive imaging technique, Electrical Impedance Tomography (EIT) aims to reconstruct the electrical conductivity distribution image in the observation field. However, the low spatial resolution restricts the development of EIT. In some specific applications, the conductivity variations are concentrated in a sub-region of the entire observation field. In response to the above problems, a design method of EIT computational focusing sensor is proposed to improve the spatial resolution in the partial region of interest (ROI). According to the conformal transformation theory, the sensor array is optimized to improve the sensitivity within the ROI to enhance the imaging accuracy in the local area. Simulation and experiment analyses are carried out to verify the effectiveness of the proposed method. The results are compared with the conventional uniform sensor. The quality of image reconstruction is evaluated by qualitative analysis and quantitative indicators. The simulation and experimental results show that the proposed optimized sensor can obtain the high precision reconstruction of inclusions in ROI and has higher recognition ability for small size and close distance inclusions in ROI than the uniform electrode array.

Electrical Impedance Tomography (EIT) is a non-invasive detection method to image the conductivity changes inside an observation region by using the electrical measurements at the boundary of this region. In some applications of EIT, the observation domain is infinite and is only accessible from one side, which leads to the so-called open EIT (OEIT) problem. Compared with conventional EIT problems, the observation region in OEIT can only be measured from limited projection directions, which makes high resolution imaging much more challenging. To improve the imaging quality of OEIT, a focusing sensor design strategy is proposed based on shape conformal theory. The conformal bijection is used to map a standard EIT sensor defined at a unit circle to a focusing OEIT sensor defined at an upper half plane. A series of numerical and experimental testes are conducted. Compared with the traditional sensor structure, the proposed focusing sensor has higher spatial resolution at the near-electrode region and is good at distinguishing multi-inclusions which are close to each other.

Electrical Impedance Tomography (EIT) is a noninvasive technique estimating the conductivity inside an observation domain from its peripheral measurements and owns many industrial and biomedical applications. Inclusion reconstruction is one of the key problems in EIT. However, the high-resolution inclusion reconstruction is challenging due to the ill-posedness and non-linearity of EIT. The shape reconstruction methods focusing on recovering the shape of the inclusion is one kind of advanced methods to enhance the EIT resolution. To achieve high-performance shape reconstruction, a Parametric Geometric Constraint Boundary Reconstruction (PGCBR) framework is proposed. The shape reconstruction is implemented by minimizing the energy function of the inclusion boundary represented by parametric snake model. To accelerate the convergence, the Newton method is adopted to solve the energy minimization problem. The first- and second-order partial derivatives of the energy terms are calculated with the EIT shape sensitivity formula. A series of numerical tests evaluated the influences of the key factors, e.g. the basis function and the number of the knot and interpolating points, of PGCBR on its reconstruction performance, and prove its robustness against the measurement noise. The following experimental tests, including both single- and multi-phase conductivity cases, further proved the advantages of PGCBR over the non-parametric method.