Magnetic resonance-driven deep learning of electromagnetic metamaterials

Metamaterials, due to their extraordinary physical properties not found in natural materials, can control their electromagnetic properties by adjusting their geometry and structural parameters. Traditional MRl systems are often affected by magnetic field distortion and signal attenuation caused by metal structures. The introduction of metamaterials provides a new way to overcome these problems. However, designing metamaterials in the traditional way can present highly complex optimization challenges. In order to solve this problem, we introduce deep learning technology to study the relationship between the properties of metamaterials and the electromagnetic response under electromagnetic drive by training neural networks, and the experimental results show that compared with traditional hand-designed metamaterials. Metamaterials optimized by deep learning show superior performance in MRl systems. The combination of deep learning and electromagnetic metamaterial design opens up new directions for the development of MRI technology and has the potential to advance the entire field of healthcare.