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Electrical Impedance Tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. Conventional EIT reconstruction methods solve a linear model by minimizing the least squares error, i.e., the Euclidian or L2-norm, with regularization. Compressed sensing provides unique advantages in Magnetic Resonance Imaging (MRI) when the images are transformed...
Magnetic Resonance Electrical Impedance Tomography (MREIT) aims to produce cross-sectional images of a conductivity distribution inside the human body with a spatial resolution of a few millimeters. Injecting currents into an imaging object at different directions, we measure induced internal magnetic flux densities using an MRI scanner. Conductivity images are reconstructed based on the relation...
Electrical Impedance Tomography (EIT) has been proposed as an alternative modality for breast imaging. Current EIT reconstruction algorithms are based in optimization procedures that aim to minimize the difference between the recorded data and a set of candidate scenarios. However, these methods produce images with diffused edges, as sharp structures are penalized by current regularization techniques...
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...
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