Three-Dimensional Complex Permittivity Prediction from Spatio-Temporal Electric Field Distributions

Quantitative imaging is a critical task in various applications, including medical diagnostics, non-destructive evaluation and material characterisation. This work addresses the problem of three-dimensional (3D) permittivity profile reconstruction of a region of interest (RoI) by leveraging a deep learning model. This is performed by training the model to establish a mapping between the spatio-temporal electric field data and the corresponding permittivity profile of the RoI. The deep learning model is designed to handle the time-varying electric field data efficiently, using a combination of convolutional layers to extract spatial features and Long Short-Term Memory (LSTM) networks, to model temporal dependencies. The effectiveness of this approach is demonstrated through comprehensive validations in different scenarios, offering a powerful tool for quantitative permittivity prediction in a wide range of applications.