Effect of Measurement Noise on Reconstruction using Machine Learning with Electrical Tomography in the Case of the Abdominal Cavity

In this paper, we compare the reconstruction efficiency obtained by the least angle regression (LARS) and Elastic Net algorithms using electrical impedance tomography (EIT). Furthermore, we investigate the impact of measurement noise on the quality of reconstruction obtained by the more efficient algorithm. We reveal the relationship between the quality of reconstruction and the magnitude of information loss in a data frame. This study was conducted on a dataset representing EIT measurements for a cross-section of the abdomen at the bladder level. The simulated dataset contains 10,000 different measurement examples for a different number of inclusions.