A fisher information matrix interpretation of the NOSER algorithm in electrical impedance tomography

In this paper, we employ the concept of the Fisher information matrix (FIM) to reformulate and improve on the “Newton´s One-Step Error Reconstructor” (NOSER) algorithm. FIM is a systematic approach for incorporating statistical properties of noise, modeling errors and multi-frequency data. The method is discussed in a maximum likelihood estimator (MLE) setting. The ill-posedness of the inverse problem is mitigated by means of a nonlinear regularization strategy. It is shown that the overall approach reduces to the maximum a posteriori estimator (MAP) with the prior (conductivity vector) described by a multivariate normal distribution. The covariance matrix of the prior is a diagonal matrix and is computed directly from the Fisher information matrix. An eigenvalue analysis is presented, revealing the advantages of using this prior to a Gaussian smoothness prior (Laplace). Reconstructions are shown using measured data obtained from a shallow breathing of an adult human subject. The reconstructions show that the FIM approach clearly improves on the original NOSER algorithm.