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

Abstract

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.