Information Content of EIT Measurements
Part of the
book series (IFMBE, volume 17)
Electrical Impedance Tomography (EIT) calculates internal conductivity from surface measurements;image reconstruction is most commonly formulated as an inverse problem using regularization techniques. Regularization adds "prior information" to adress the solution ill-conditioning. This paper presents a novel approach to understand and quantify this information. We ask: how many bits of information (in the Shannon sense) do we get from an EIT data frame. We define the term information in measurements (IM) as the: decrease in uncertainty about the contents of a medium, due to a set of measurements. Before the measurements, we know the prior information (inter-class model, q). The measured data tell us about the medium (which, corrupted by noise, gives the intra-class model, p). The measurement information is given by the relative entropy (or Kullback-Leibler divergence).
KeywordsPrior Information Electrical Impedance Tomography Measurement Channel Regularization Technique Leibler Divergence
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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