Phantom-metasurface cooperative system trained by a deep learning network driven by a bound state for a magnetic resonance-enhanced system

With the development of medical imaging technology, magnetic resonance imaging (MRI) has become an important tool for diagnosing and monitoring a variety of diseases. However, traditional MRI techniques are limited in terms of imaging speed and resolution. In this study, we developed an efficient body mode metasurface composite MRI enhancement system based on deep learning network training and realized the design and control of metasurface in the MHz band. Firstly, forward neural network is used to predict the electromagnetic response characteristics quickly. On this basis, the network is reverse-designed and the structural parameters of the metasurface are predicted. The experimental results show that the combination of deep neural network and electromagnetic metasurface significantly improves the design efficiency of metasurface and has great application potential in the MRI system.