A Machine Learning-Assisted Inversion Method for Solving Biomedical Imaging Based on Semi-Experimental Data

Machine learning approaches have been extensively utilized in the field of inverse scattering problems. Typically, the training dataset is generated synthetically using ideal radiation sources such as plane waves or cylindrical waves. However, the testing data often consist of experimental data that take into account the antenna port couplings and waveform distortions within the system. While noise can be artificially added to synthetic data, it may not accurately represent the real experimental noise. Consequently, the application of machine learning-assisted inversion techniques may encounter challenges when the training dataset differs significantly from the experimental data. In this paper, we propose an experimental system specifically designed for human body imaging. A semi-experimental training dataset is constructed using full-wave simulation software, incorporating the relative permittivities of common human tissues. Furthermore, the system noise is meticulously considered through full-wave simulation, enhancing the authenticity of the dataset. A back-propagation scheme is firstly employed to obtain the rough reconstructed images. Then, the U-net convolutional neural network (CNN) is employed to map these rough images to high-resolution ones. Numerical results demonstrate that, in comparison to networks trained solely on synthetic data, the network trained using semi-experimental data achieves superior reconstruction results with lower errors and improved image quality.