## 1 Introduction  
Magnetic resonance imaging is one of the most widespread diagnostic techniques in the biomedical and clinical fields [1, 2]. Indeed, compared to PET and X-rays, it exploits the interactions between tissues and a radio-frequency magnetic field. Therefore, due to the absence of ionizing radiation, it is safer for the patients [3]. In addition, differently from other imaging modalities, it does not require solving an inverse problem to infer tissues properties [4]. Hence, the use of direct algorithms makes this technology a reliable and convenient means for diagnostics. Naturally, double-tuned MRI modalities have also been developed, which are able to combine signals coming from multiple chemical nuclei. In this way, anatomical and functional information can be simultaneously acquired, thus enhancing the imaging capabilities [5, 6, 7, 8]. As an example, many human diseases involve alterations in the regulation of \({}^{23}\)Na concentration, although detecting the weak signal emitted by the sodium is, todate, still a challenge. Although these are promising advantages, the scientific community is nevertheless focused on continuously improving MRI performance, particularly in terms of signal-to-noise ratio (SNR), transmission efficiency (TXE), and RF magnetic field spatial homogeneity. As can be expected, optimizing transmission efficiency while preserving magnetic field homogeneity and enhancing signal-to-noise ratio is extremely challenging. Moreover, these performance parameters must also be considered together with the Specific Absorption Rate minimization, necessarily resulting in a trade-off [9; 10; 11; 12; 13; 14]. Therefore, researchers are partaking in a great effort to improve MRI performance, and consequently, the image quality. In particular, although the imaging post-processing elaboration plays a very important role in the enhancement of imaging, we will focus on hardware aspects in this work. In this sense, to improve the transmission efficiency and the detection of smaller signals emitted by the investigated tissue, a solution based on high-permittivity dielectric pads included within the scanner during the procedure has been presented in the literature [15; 16; 17]. In particular, this technology has been successfully declined for brain scanning, by using a high permittivity material helmet (HPM-helmet) that covered the patient's head [18; 19; 20]. According to the Maxwell equations, the magnetic field is enhanced by placing a dielectric material which is able to increase the displacement currents. Although a transmission efficiency improvement up to 65% inside the tissue was observed, this solution is nevertheless extremely cumbersome and heavy, producing discomfort for the patient and difficulties to be integrated within the scanner. As an alternative to enhancing the SNR, several high sensitivity receiving coils designs, characterized by shapes conformal to the anatomy of the specific investigated tissue, have also been described in the literature [21; 22; 23]. This approach can be extremely effective but is usually limited to surface coils with a single operating frequency. Recently, the use of electromagnetic metasurfaces has also been proposed in MRI. Typically, metasurfaces consist of a 2D periodic arrangement of properly designed resonant elements able to show non-natural electromagnetic properties, such as negative permeability or permittivity and also negative refractive index [24; 25]. Due to their compact dimensions with respect to their 3D counterpart, metasurfaces have recently received a lot of attention in several fields, as well in biomedical applications [26; 27; 28]. In their first application in MRI, the replacement of the heavy and cumbersome dielectric pads has occurred by implementing dielectric metasurfaces [29]. The physical idea is that the conductance currents flowing along a periodic array of thin metal strips can create a magnetic field similar to the one produced by the displacement currents inside a high-permittivity material.