Progress Toward a Deployable SQUID-Based Ultra-Low Field MRI System for Anatomical Imaging

Magnetic resonance imaging (MRI) is the best method for non-invasive imaging of soft tissue anatomy, saving countless lives each year. But conventional MRI relies on very high fixed strength magnetic fields, ≥ 1.5 T, with parts-permillion homogeneity, requiring large and expensive magnets. This is because in conventional Faraday-coil based systems the signal scales approximately with the square of the magnetic field. Recent demonstrations have shown that MRI can be performed at much lower magnetic fields (~100 μT, the ULF regime). Through the use of pulsed prepolarization at magnetic fields from ~10-100 mT and SQUID detection during readout (proton Larmor frequencies on the order of a few kHz), some of the signal loss can be mitigated. Our group and others have shown promising applications of ULF MRI of human anatomy including the brain, enhanced contrast between tissues, and imaging in the presence of (and even through) metal. Although much of the required core technology has been demonstrated, ULF MRI systems still suffer from long imaging times, relatively poor quality images, and remain confined to the R&D laboratory due to the strict requirements for a low noise environment isolated from almost all ambient electromagnetic fields. Our goal in the work presented here is to move ULF MRI from a proof-of-concept in our laboratory to a functional prototype that will exploit the inherent advantages of the approach, and enable increased accessibility. Here we present results from a seven-channel SQUID-based system that achieves pre-polarization field of 100 mT over a 200 cm3 volume, is powered with all magnetic field generation from standard MRI amplifier technology, and uses off the shelf data acquisition. As our ultimate aim is unshielded operation, we also demonstrated a seven-channel system that performs ULF MRI outside of heavy magnetically-shielded enclosure. In this paper we present preliminary images and compare them to a model, and characterize the present and expected performance of this system.