CHAPTER 7. Detection Using SQUIDs and Atomic Magnetometers

Abstract

This chapter discusses mobile MRI in the context of operation outside of a traditional hospital or laboratory setting, and not in the sense of truck-deployed systems that are similar to what one would find in a hospital, just taking the infrastructure along with them. We take the approach of describing MRI in a rather different regime, which we call ultra-low field (ULF). Specifically, ULF MRI involves very low measurement magnetic fields from 10 µT to 10 mT, i.e. Larmor frequencies from ∼400 Hz to 400 kHz, uses pre-polarization from tens to hundreds of mT to enhance signal, and relies on very sensitive sensors. Specifically both the Superconducting Quantum Interference Devices (SQUIDs) and the atomic magnetometer (AM) have been demonstrated to improve detection performance at the correspondingly low frequencies. ULF systems pay a significant price in signal-to-noise (SNR) and thus speed and spatial resolution; however, they may provide advantages to mobile MRI applications for the following reasons: (1) ultra-low magnetic fields that do not require very high homogeneity, are easier to generate, and therefore lighter and less expensive systems may be realized; 2) ultra-low magnetic fields are safer for public places and are more tolerant and safer in the presence of metal; (3) at very low fields unique pulse sequences such as those based on changing the orientation of the measurement field can be employed; and (4) because T1 changes as a function of the magnetic field, it may be possible to exploit different image contrast at ULF, or vary image contrast by changing the magnetic field; (5) ultra-low magnetic fields may be safer for certain populations, e.g. patients with medical implants, neonates; and (6) in some cases, generation of high magnetic field is prohibitive, and there is no other option for MRI. We will describe MRI systems based on the approach of ultra-low magnetic field and sensitive readout with SQUIDs or AMs. We will first introduce the basics of each sensor technology and the hardware considerations in performing MRI with them. Then we will describe several applications motivated by the advantages described above, discussing both the existing challenges and potential for improvement. Although at the time of this writing there are very few examples of ultra-low field MRI based on SQUIDs or AMs, let alone mobile ones, we will strive to illuminate applications to mobile MRI that might benefit from this approach. We will conclude with a discussion of how to model such systems, so that the reader can determine the “best case” images that can be expected.