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.

High-Q metasurface signal isolator for 1.5T surface coil magnetic resonance imaging on the go.

The combination of surface coils and metamaterials remarkably enhance magnetic resonance imaging (MRI) performance for significant local staging flexibility. However, due to the coupling in between, impeded signal-to-noise ratio (SNR) and low-contrast resolution, further hamper the future growth in clinical MRI. In this paper, we propose a high-Q metasurface decoupling isolator fueled by topological LC loops for 1.