MRI scanners enable fast, noninvasive, and high-resolution imaging of organs and soft tissue. The images are reconstructed from NMR signals generated by nuclear spins that precess in a static magnetic field B 0 in the presence of magnetic field gradients. Most clinical MRI scanners operate at a magnetic field B0 ‫؍‬ 1.5 T, corresponding to a proton resonance frequency of 64 MHz. Because these systems rely on large superconducting magnets, they are costly and demanding of infrastructure. On the other hand, low-field imagers have the potential to be less expensive, less confining, and more mobile. The major obstacle is the intrinsically low sensitivity of the low-field NMR experiment. Here, we show that prepolarization of the nuclear spins and detection with a superconducting quantum interference device (SQUID) yield a signal that is independent of B 0, allowing acquisition of highresolution MRIs in microtesla fields. Reduction of the strength of the measurement field eliminates inhomogeneous broadening of the NMR lines, resulting in enhanced signal-to-noise ratio and spatial resolution for a fixed strength of the magnetic field gradients used to encode the image. We present high-resolution images of phantoms and other samples and T 1-weighted contrast images acquired in highly inhomogeneous magnetic fields of 132 T; here, T 1 is the spin-lattice relaxation time. These techniques could readily be adapted to existing multichannel SQUID systems used for magnetic source imaging of brain signals. Further potential applications include low-cost systems for tumor screening and imaging peripheral regions of the body. T he conventional MRI receiver coil operates on the principle of Faraday induction (1-4): the signal is therefore proportional to the product of sample magnetization and the frequency of nuclear spin precession. In the high-temperature limit, the thermal magnetization of the sample scales linearly with the magnetic field strength. Similarly, the nuclear precession frequency is proportional to the strength of the applied field. In the case of conventional detection, therefore, the NMR signal strength scales as B 0 2 . The quadratic dependence of NMR signal on magnetic field has fuelled the drive to higher field strengths in MRI scanners for the last two decades, despite the disadvantages of converging T 1 times and increased energy deposition at higher frequencies.At the same time, there has been continued interest in the development of MRI scanners that operate at low magnetic field strengths, of the order of the earth's field (Ϸ50 T). Previous approaches to low-field MRI have relied heavily on techniques such as optical pumping (refs. 5 and 6, and ref. 7 and references therein) or prepolarization of the nuclear spins in a strong transient field (8-10) to generate enhanced, nonequilibrium nuclear magnetization and thereby boost the strength of the NMR signal. Tseng et al. (6) demonstrated MRI of hyperpolarized 3 He gas in a field of 2 mT. In the low-field imaging work of Macovski et al. (8,9), the spins we...

There has been a long-standing demand for noninvasive neuroimaging methods that can detect neuronal activity at both high temporal and high spatial resolution. We present a two-dimensional fast line-scan approach that enables direct imaging of neuronal activity with millisecond precision while retaining the high spatial resolution of magnetic resonance imaging (MRI). This approach was demonstrated through in vivo mouse brain imaging at 9.4 tesla during electrical whisker-pad stimulation. In vivo spike recording and optogenetics confirmed the high correlation of the observed MRI signal with neural activity. It also captured the sequential and laminar-specific propagation of neuronal activity along the thalamocortical pathway. This high-resolution, direct imaging of neuronal activity will open up new avenues in brain science by providing a deeper understanding of the brain’s functional organization, including the temporospatial dynamics of neural networks.

In magnetic resonance imaging-based electrical properties tomography (MREPT), tissue electrical properties (EPs) are derived from the spatial variation of the transmit RF field (B1+). Here we derive theoretically the relationship between the signal-to-noise ratio (SNR) of the electrical properties obtained by MREPT and the SNR of the input B1+ data, under the assumption that that latter is much greater than unity, and the noise in B1+ at different voxels is statistically independent. It is shown that for a given B1+ data, the SNR of both electrical conductivity and relative permittivity is proportional to the square of the linear dimension of the region of interest (ROI) over which the EPs are determined, and to the square root of the number of voxels in the ROI. The relationship also shows how the SNR varies with the main magnetic field (B0) strength. The predicted SNR is verified through numerical simulations on a cylindrical phantom with an analytically calculated B1+ map, and is found to provide explanation of certain aspects of previous experimental results in literature. Our SNR formula can be used to estimate minimum input data SNR and ROI size required to obtain tissue EP maps of desired quality.