Homonuclear J-Coupling Spectroscopy at Low Magnetic Fields using Spin-Lock Induced Crossing*.

Nuclear magnetic resonance (NMR) spectroscopy usually requires high magnetic fields to create spectral resolution among different proton species. Although proton signals can also be detected at low fields the spectrum exhibits a single line if J-coupling is stronger than chemical shift dispersion. In this work, we demonstrate that the spectra can nevertheless be acquired in this strong-coupling regime using a novel pulse sequence called spin-lock induced crossing (SLIC). This techniques probes energy level crossings induced by a weak spin-locking pulse and produces a unique J-coupling spectrum for most organic molecules. Unlike other forms of low-field J-coupling spectroscopy, our technique does not require the presence of heteronuclei and can be used for most compounds in their native state. We performed SLIC spectroscopy on a number of small molecules at 276 kHz and 20.8 MHZ and show that the simulated SLIC spectra agree well with measurements.

Analytical models of probe dynamics effects on NMR measurements.

This paper provides a detailed analysis of three common NMR probe circuits (untuned, tuned, and impedance-matched) and studies their effects on multi-pulse experiments, such as those based on the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. The magnitude of probe dynamics effects on broadband refocusing pulses are studied as a function of normalized RF bandwidth. Finally, the probe circuit models are integrated with spin dynamics simulations to design hardware-specific RF excitation and refocusing pulses for optimizing user-specified metrics such as signal-to-noise ratio (SNR) in grossly inhomogeneous fields. Preliminary experimental results on untuned probes are also presented.

An easily reproducible, hand-held, single-sided, MRI sensor.

Single-sided MRI sensors allow the imaging of samples that are larger than the magnet. Thus, they enable truly portable imagers with potential applications in medicine, quality assurance (QA), agriculture, material science, and other fields. However, despite recent advancements, single-sided MRI systems are relatively uncommon. This is partially due to the limited number of commercial products. Also, current implementations often require large and/or complex magnet arrays which require machining techniques such as milling or drilling. These techniques must be performed to tight tolerances to ensure accuracy of the B0 field. Furthermore, these systems generally have hand-wound RF or gradient coils that are not trivial to construct. The main goals of this work are to reduce the size of single-sided MRI sensors while simultaneously making them more accessible for others to build. To this end, we present a hand-held, single-sided, MRI sensor that is constructed using an easy-to-assemble magnet array, a 3D-printed housing, and printed circuit boards (PCBs) that contain the RF coil, gradient coils, and matching network. By implementing all coils directly on PCBs, the geometry can be easily optimized and then manufactured at low cost. Both spin density-weighted and T1-weighted images of various samples are presented to demonstrate the capabilities of the proposed sensor.

Electropermanent magnets for variable-field NMR.

In this work, a dynamically tunable B0 field is used to perform variable-field NMR. The system consists of an array of electropermanent AlNiCo-5 magnets whose magnetizations are individually programmed using pulse-power control. This design allows the field strength to be varied for field-dispersion measurements. An ultra-broadband front-end is utilized that maintains efficient power transmission over a broad range of frequencies for robust operation without probe tuning. We perform T1-T2 correlation measurements at various B0 field strengths (0.5-2 MHz) and demonstrate discrimination of different dairy products. We observe variation in the frequency dependence of the proton spin-lattice relaxation for the different products as a function of the degree of protein hydration. This variable-field technique provides a low-cost alternative to fast field-cycling NMR and could open possibilities for novel contrast measurements and spatial encoding in magnetic resonance imaging.

An autonomous, highly portable NMR spectrometer based on a low-cost System-on-Chip (SoC).

This paper describes the development of a portable and self-optimizing NMR spectrometer based on a miniaturized custom analog front-end and a System-on-Chip (SoC)-based digital back-end. The SoC integrates a field-programmable gate array (FPGA) fabric with a hard processor running a Linux operating system, thus enabling fully-autonomous operation without the need for an external computer. In the proposed approach, data captured by the FPGA fabric during regular operation is transported to the hard processor using an integrated on-chip bus for further processing. The processed results are then used to automatically estimate parameter values that optimize a suitable cost function, such as signal-to-noise ratio (SNR) per unit time. Finally, the optimized values of both electrical and NMR-related tuning parameters (e.g., preamplifier gain and frequency response, pulse length and amplitude, operating frequency, etc.) are programmed back into the front-end and back-end hardware. Experimental NMR results from various samples in a ∼0.1 T permanent magnet are presented to verify the operation of the proposed spectrometer. These demonstrate on-board Laplace inversion and automated frequency tuning to compensate for temperature changes. Preliminary 14N NQR results are also presented.

Automated classification of food products using 2D low-field NMR.

In this work, low-field proton (1H) and sodium (23Na) relaxation and diffusion measurements are used to detect and classify different types of food products. A compact and low-cost system based on a small 0.5 T permanent magnet has been developed to autonomously authenticate such products. The system uses a simple but efficient double-tuned matching network suitable for 1H/23Na NMR. Various machine learning algorithms are used to classify food samples based on T1-T2 and D-T2 data generated by the system, and the accuracy and prediction speed of these algorithms are studied in detail. The influence of temperature drift upon prediction accuracy is also studied. Experimental results demonstrate reliable classification of cooking oils, milk, and soy sauces.