A Method of Generating Semi-Experimental Biomedical Datasets

This paper proposed a method to generate semi-experimental biomedical datasets based on full-wave simulation software. The system noise such as antenna port couplings is fully considered in the proposed datasets, which is more realistic than synthetical datasets. In this paper, datasets containing different shapes are constructed based on the relative permittivities of human tissues. Then, a back-propagation scheme is used to obtain the rough reconstructions, which will be fed into a U-net convolutional neural network (CNN) to recover the high-resolution images. Numerical results show that the network trained on the datasets generated by the proposed method can obtain satisfying reconstruction results and is promising to be applied in real-time biomedical imaging.