title: A fisher information matrix interpretation of the NOSER algorithm in electrical impedance tomography.
abstract: 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.
authors.affiliation: Department of Health and Social Sciences, Middlesex University, London NW44BT, United Kingdom. p.hashemzadeh@mdx.ac.uk
authors.forename: Parham
authors.initials: P
authors.lastname: Hashemzadeh
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Kantartzis
authors.forename: Ali
authors.initials: A
authors.lastname: Zifan
authors.forename: Panos
authors.initials: P
authors.lastname: Liatsis
authors.forename: Sven
authors.initials: S
authors.lastname: Nordebo
authors.forename: Richard
authors.initials: R
authors.lastname: Bayford
mesh_terms: D000818:Animals; D003198:Computer Simulation; D058266:Dielectric Spectroscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014054:Tomography / Q000379:methods*