A Krylov subspace type method for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a well-known imaging technique for detecting the electrical properties of an object in order to detect anomalies, such as conductive or resistive targets. More specifically, EIT has many applications medical detection and location bodily tumors since it affordable non-invasive method, which aims recover internal conductivity body using voltage measurements resulting from applying low frequency current at electrodes placed its surface. Mathematically, reconstruction severely ill-posed inverse problem yields poor quality image reconstruction. To remedy this difficulty, least part, we regularize solve nonlinear minimization by aid Krylov subspace-type method linear sub during each iteration. In EIT, tumor general anomaly can be modeled piecewise constant perturbation smooth background, hence, regularized on subspace relatively small dimension Flexible Golub-Kahan process that provides solutions have sparse representation. For comparison, use modified Gauss–Newton algorithm benchmark. Using simulations, demonstrate effectiveness proposed method. The obtained reconstructions indicate better adapted results higher resolution images faster convergence compared algorithm.