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Improved rotational invariance for statistical inverse in electrical impedance tomography

In this paper we show that rotational invariance can be improved in a neural network based electrical impedance tomography (EIT) reconstruction approach by a suitably chosen permutation of the input data. The input space is partitioned to nonoverlapping sectors, and the input signal is permuted so that it lies in one sector independent of the original rotation angle. We demonstrate the advantages of the method with computer simulations. The proposed approach yields better results in the inverse problem, and allows use of smaller networks with fewer training samples.