Introduction

Multi-echo GRE has many widely used quantitative brain imaging applications including Dixon MRI,¹ R2* mapping,² and susceptibility mapping (QSM).³ As with most MRI applications, such approaches are sensitive to motion. Prospective motion correction (PMC) is a promising solution in such cases;⁴ however, data acquired at longer echo times is also sensitive to additional phase errors caused by motion-induced spatiotemporal variations in B0 as subject orientation diverges from that for which shimming was optimized.⁵

Navigator-based approaches to simultaneous motion and B0 correction^6-10 are preferable to external monitoring systems¹¹ in that they require no additional hardware and impose minimal changes to the clinical workflow. In particular, spherical navigators¹² (SNAVs) have shown promise for PMC with relatively short acquisition and processing times.¹³ Incorporating an additional free induction decay (FID) readout enables simultaneous field monitoring.¹⁴ Previous work has used these FID-SNAVs for combined PMC and retrospective field correction of a susceptibility weighted imaging protocol.¹⁵ In this study, we extend their application from single- to multi-echo GRE for multiparametric mapping.¹⁶

Methods

All experiments were performed on a 3T scanner (MAGNETOM PrismaFit, Siemens Healthcare GmbH, Erlangen, Germany) using the product 32-channel head coil. Imaging parameters (FOV 25.6x25.6x19.2 cm, resolution 1.14x1.14x2.00 mm, TR 51 ms, TEs 3.28/4.72/6.22/7.72/9.49/16.75/23.90/31.10/38.16/45.40 ms, FA 15°, BW 1015 Hz/Pixel, GRAPPA R = 2, scan time 10:07) and processing pipeline for mapping fat fraction, R2*, and susceptibility (using MEDI,¹⁷ λ = 2e4, 2 iterations) were based on a previously developed protocol.¹⁶ FID-SNAVs (acquired as two hemispheres) were interleaved with imaging acquisition at a frequency of 1.96 Hz (scan time increase 2:32); an additional baseline scan (0:17) was also acquired to accelerate rotation estimation.¹⁸ SNAV data were processed online to provide motion estimates for PMC (using real-time sequence processing, margin time 10 ms).¹³ FID data were processed offline to perform zeroth order field correction of imaging data on a channel-wise basis prior to GRAPPA reconstruction.¹⁵ An overview is given in Figure 1.

Motion measurement accuracy was evaluated using a pineapple phantom; rotations were simulated via gradient update while translation was controlled using a linear motion stage.¹⁹ A separate QSM phantom²⁰ was also imaged to ensure the navigators had no impact on image contrast or the resulting parametric maps. One volunteer was scanned with approval from the local Research Ethics Board. Following an initial reference acquisition (no motion), two images were acquired with motion: one uncorrected and one with PMC (processed with and without field correction). To produce similar trajectories for scans with and without correction, motion was guided using a visual indicator projected into the scanner,²¹ with the prompt itself derived from a true undeliberate motion trajectory obtained from an open-source database.²² Lastly, one additional scan was acquired with no motion to provide a measure of interscan variation. All scans were processed to produce quantitative maps before registering the results to the initial reference scan.

Results

Phantom evaluation of motion estimation demonstrated sub-degree, millimeter accuracy (mean absolute error 0.04 ± 0.06 deg, 0.21 ± 0.14 mm). Comparison of scans with and without navigator acquisition showed no effect on image contrast or the resulting quantitative maps.

Measured motion traces for the volunteer study are given in Figure 2. Motion estimates were computed in 26.1 ± 0.4 ms (total latency < 50 ms when considering margin time). Field offsets (shown for three sample channels) are given in Figure 3. Similar trajectories, offsets, were observed for scans with and without PMC.

Clear improvement in image quality was seen with PMC alone for early echoes; later echoes also showed improvement but suffered from additional artifacts caused by motion induced B0 inhomogeneity. Additional retrospective correction of field offsets further improved image quality. Sample images and corresponding quantitative maps are displayed in Figure 4.

Discussion

In this work, combined FID-SNAVs were implemented for PMC and retrospective correction of resulting field offsets in a multi-echo GRE protocol for R2* mapping and QSM. Motion correction alone provided a notable improvement in image quality, though retrospective correction of field offsets did reduce the impact of residual artifacts. High field applications would likely experience an even greater benefit.⁵

Remaining artifacts, most apparent in the anterior frontal lobe, will likely benefit from prospective correction of higher order field offsets.^23,24 Future work will also investigate FID-based triggering of navigator acquisition²⁵ to optimize the tradeoff with scan time, as well as prospective update of navigator acquisition to reduce the time required for baseline acquisition.

Conclusion

FID-SNAVs are a promising approach to combined motion and B0 correction for quantitative mapping techniques with no requirement for additional hardware or modifications to clinical workflow.

Acknowledgements

This work was completed with support from the Natural Sciences and Engineering Council of Canada, the Brain Canada Foundation, and the Center for Metabolic Mapping at Robarts Research Institute.

References

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