Introduction

The sensitivity of MRI to magnetic susceptibility is both a blessing and a curse: While small-scale changes in the magnetic field permit useful image contrasts, such as the blood-oxygenation-level dependent (BOLD) effect, larger scale field distortions, such as those due to air-tissue interfaces, can cause image artifacts such as signal loss in gradient-echo (GRE) imaging and geometric distortion in echo-planar imaging (EPI). The proximity of the spinal cord to the lungs – the body’s most extensive internal air-tissue interface – makes it particularly susceptible to these field effects, which generally vary in time, along with respiration (e.g., Figure 1).^1–3 Given the extent of these artifacts, along with the inherent subtlety of the signal changes associated with BOLD activation (about 1%), fMRI of the spinal cord remains technically challenging, as underscored in a recent review: “respiration-induced change in B0 is one of the main reasons very few people dare to venture into fMRI experiments below the cervical level.”⁴ This study presents the first results updating shims in real-time to correct for respiration-induced distortions in the spinal cord.

Methods

Real-time shimming hardware is exhibited in Figure 2.⁵ Calculation of the shim updates was informed by the work of Van Gelderen et al.⁶ which suggested a possible linear relation between the time-varying magnetic field and the respiratory cycle as tracked by a pressure reading from respiratory bellows strapped to the subject’s abdomen: $$$ \frac{ \partial b_{\chi} (\textbf{r}, t) }{ \partial t } = c_{\chi}(\textbf{r}) \frac{ \partial p(t) }{ \partial t } $$$, which, when integrated, yields $$$ b_{\chi} (\textbf{r}, t) = c_{\chi}(\textbf{r})p(t) + b_{\chi|o}(\textbf{r}) $$$. It follows that the coupling coefficients $$$c_{\chi}(\bf{r})$$$ and the static field offset $$$b_{\chi|o}(\textbf{r})$$$ might be estimated by GRE field maps acquired in as few as two distinct apneas—a sort of calibration procedure by which all future field perturbations can be inferred based on the associated pressure reading (Figure 3).

Experiments: Six healthy subjects were scanned on a 3 T system (Siemens TIM Trio). Standard 2nd-order static shimming was first performed using the MRI shims. To calibrate the real-time shim updates, GRE field maps were acquired in inspired and expired respiratory states (e.g. Figures 1 and 3) with acquisition and phase processing parameters similar to those described in a previous work.⁵ To demonstrate the utility of real-time shimming, a 2D sagittal GRE-EPI sequence typical of fMRI was performed during normal respiration, with and without real-time shimming, for which shim updates were issued every 250 ms based on the respiratory trace sampled at 100 Hz. The EPI sequence used the following parameters: TE=15 ms, TR=500 ms; flip angle 70°; bandwidth=1502 Hz/pixel; R=2 acceleration factor using GRAPPA; partial Fourier=6/8; spatial resolution=1.5x1.5 mm² in-plane, with 7 sagittal slices of thickness 5.0 mm, for an effective FOV=35x156x192 mm³; 100 image volumes were acquired, for a total acquisition time of 50 s. The temporal signal-to-noise ratio (tSNR) of the EPI data was calculated as the average voxel intensity across the time series divided by its standard deviation.

Results and Discussion

Over the spinal cord, real-time shimming improved the mean EPI tSNR by 18.9 +/- 13.2 %, while increasing tSNR over the entire region considered in the shim optimization by 12.2 +/- 10.1%. Figure 4 summarizes these improvements and Figure 5 shows visual results for the first two subjects.

This study demonstrates the benefit of real-time shimming for spinal cord EPI. Regarding improvements to tSNR, a considerable intersubject variability was observed likely due to anatomical differences that determine the extent of respiration-induced distortion. Though increased tSNR was observed across subjects, the real-time protocol could nevertheless be improved, for example, by updating the shims more frequently, and by accounting for transmission delays. Furthermore, apart from ignoring other contributions to the time-varying magnetic field (e.g. eddy currents, cardiac pulsation), the validity of the linear pressure-to-field relation used to update the shims has been taken for granted; other models of the time-evolving field,⁷ or means of tracking it,^8–10 may ultimately prove more appropriate.

In addition to signal enhancement and artifact reduction for EPI, real-time shimming stands to benefit T2*-weighted imaging generally, as well as spectroscopic studies of the spinal cord.

Acknowledgements

We thank the team at Resonance Research Inc. – Piotr Starewicz, Kai-Ming Lo, Karl Metzemaekers, and Donald Jette – for their work constructing the shim hardware. Funded by the Canada Research Chair in Quantitative Magnetic Resonance Imaging (JCA), the Canada Foundation for Innovation [32454], the Canadian Institutes of Health Research [CIHR FDN-143263], the Fonds de Recherche du Québec - Santé [28826], the Fonds de Recherche du Québec - Nature et Technologies [2015-PR-182754], the Natural Sciences and Engineering Research Council of Canada [435897-2013], the Quebec BioImaging Network, and Polytechnique MEDITIS.

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