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An adaptive parametric level-set method for lung health monitoring with smartphone-based electrical impedance tomography

In this paper, an adaptive parametric level-set method is presented for lung health monitoring with smartphone-based electrical impedance tomography (EIT). Firstly, assuming that the conductivity distribution to be reconstructed is piecewise constant, the shape of lung-like targets is represented by a PLS function using a Gaussian radial basis function. Secondly, the unknown parameters (e.g. centers, widths, and weights) are computed adaptively without a prior information about targets. Thirdly, rectified Adam is proposed to adaptively adjust the learning rate during the iterative process. The proposed method is evaluated quantitatively by simulated lung imaging, water tank tests, and human breathing data. In addition, the robustness of the proposed method is performed by considering different gradient descent methods and different number of RBF centers. The reconstruction results show that the proposed method not only overcomes some problems associated with the traditional level-set method (e.g. reinitialization and use of signed distance function), but also avoids empirical parameter selections in the PLS method, resulting in faster imaging speed and better imaging quality with an average image correlation coefficient greater than 0.90. It is found that the one-second rate of the proposed method is closer to the true value with an average error of no more than 2% when the forced vital capacity tests are repeatedly performed on five volunteers with healthy lungs. The proposed method is promising in providing the reliable assessment of lung health monitoring with smartphone-based EIT.