Reconfigurable Metamaterials in Magnetic Resonance Imaging - Benefits and current developments-

Reconfigurable metamaterials offer many degrees of freedom and allow to overcome limitations of present technological capabilities in Magnetic Resonance Imaging (MRI). Various metrics such as speed, efficiency, and local signal-to-noise ratio (SNR) can be drastically improved and tailored applications rather than global effects become possible. So far, in the literature there exist a few metamaterial realizations for MRI, and only a small subset includes non-static mechanisms and a very low degree of reconfigurability, if any. At least, resonance frequency fine-tuning must be provided to handle patient-specific and vendor-related effects. Manual control is too far from clinical use. Only a digital and wireless interface, supplemented by an on-board logic, will have a meaningful impact. True reconfigurability does not only include tuning at the unit cell level and dynamic spatial sensitivity profiles w.r.t. the interaction with the incident transmit and/ or receive fields, but also the precise control in the time domain. Thereupon, the integration into some sequence control and development framework is mandatory, ideally in a vendor-independent way. The state of the art, potential benefits, and various use-cases of static, dynamic, reconfigurable, and interactive metamaterials as well as control strategies are presented. Applications range from simple SNR enhancement to spatial encoding patterns for reduced gradient fields. First-generation prototypes of such metamaterials demonstrate the working principle, and on-bench characterization results as well as MRI measurements are shown. For a specific application, a pre-defined target function must be extremized. A prime example is a desired spatio-temporal sensitivity profile for SNR modifications. Thus, suitable optimization strategies have to be implemented, which include conventional as well as AI-driven approaches. Simulations, on-bench measurements, deep learning results, and MRI verification are demonstrated for a proof-of-principle application.