Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. It has been used for the continuous monitoring of physiological functions such as lung ventilation and perfusion. The image contrast represents the time change of the electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applications have arisen using inexpensive portable EIT devices. EIT acquires multiple trans-impedance measurements across the body from an array of surface electrodes around a chosen imaging slice. The conductivity image reconstruction from the measured data is a fundamentally ill-posed inverse problem notoriously vulnerable to measurement noise and artifacts. Most available methods invert the ill-conditioned sensitivity or Jacobian matrix using a regularized least-squares data-fitting technique. Their performances rely on the regularization parameter, which controls the trade-off between fidelity and robustness. For clinical applications of EIT, it would be desirable to develop a method achieving consistent performance over various uncertain data, regardless of the choice of the regularization parameter. Recently, Kyounghun Lee et.al developed a fidelity-embedded regularization (FER) method and a motion artifact removal filter, with a careful analysis of the structure of the Jacobian matrix. This method showed practical merits in experimental studies of chest EIT imaging. For details, see the paper, A Fidelity-embedded Regularization Method for Robust Electrical Impedance Tomography.
The EIT data were obtained by using Sciospec 16channel EIT system.