A deep learning-based electrical impedance imaging method, comprising the following steps: obtaining an original boundary measurement voltage data set of an area to be tested, calculating an initial conductivity distribution sequence of the area to be tested, and performing normalization processing to form a corresponding training data set (S1); adding noise in standard normal distribution to the initial conductivity distribution sequence, and training a variational autoencoder using the training data set by means of a deep learning network (S2); according to the boundary measurement voltage data set, generating hidden layer data in a network using an encoder in the trained variational autoencoder to obtain an input voltage data feature (S3); and establishing a mapping relationship between the boundary measurement voltage data set and a corresponding conductivity distribution sequence using the voltage data feature and a decoder in the trained variational autoencoder (S4). The method solves the problems of low image reconstruction precision, poor noise-resistant performance, and low priori knowledge use efficiency in conventional electrical impedance imaging, thus improving the image reconstruction precision of electrical impedance imaging.
DEEP LEARNING-BASED ELECTRICAL IMPEDANCE IMAGING METHOD