A review of electrical impedance tomography in lung applications: Theory and algorithms for absolute images

Electrical Impedance Tomography (EIT) is under fast development. The present paper is a review of some procedures that have contributed to improve spatial resolution and material properties accuracy, admitivity or impeditivity accuracy. A review of EIT medical applications is presented and they were classified into three broad categories: ARDS patients, obstructive lung diseases and perioperative patients. The use of absolute EIT image may enable the assessment of absolute lung volume, which may significantly improve clinical acceptance of EIT. The Control Theory, State Observers more specifically, have a developed theory that can be used for designing and operating EIT devices. Electrode placement, current injection strategy and electrode electric potential measurements strategy should maximize the number of observable and controllable directions of the state vector space. A non-linear stochastic state observer, the Unscented Kalman Filter, is used directly for reconstructing absolute EIT images. Historically, difference images were explored first since they are more stable in the presence of modelling errors. Absolute images require more detailed models of contact impedance, stray capacitance and properly refined finite element mesh, where the electric potential gradient is high. Parallelization of the forward program computation is necessary since the solution of the inverse problem often requires frequent solutions of the forward problem. Several reconstruction algorithms benefit from the Bayesian inverse problem approach and the concept of prior information. Anatomic and physiological information are used to form the prior information. An already tested methodology is presented to build the prior probability density function using an ensemble of CT scans and in vivo impedance measurements. Eight absolute EIT image algorithms are presented. (C) 2019 Elsevier Ltd. All rights reserved.