A comparison of navigators, snap-shot field monitoring, and probe-based field model training for correcting B0-induced artifacts in T2*-weighted images at 7 T

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Abstract

Purpose: To compare methods for estimating B0 maps used in retrospective correction of high-resolution anatomical images at ultra-high field strength. The B0 maps were obtained using three methods: (1) 1D navigators and coil sensitivities, (2) field probe (FP) data and a low-order spherical harmonics model, and (3) FP data and a training-based model. Methods: Data from nine subjects were acquired while they performed activities inducing B0 field fluctuations. Estimated B0 fields were compared with reference data, and the reductions of artifacts were compared in corrected T2* images. Results: Reduction of sum-of-squares difference relative to a reference image was evaluated, and Method 1 yielded the largest artifact reduction: 27 ± 15%, 20 ± 18% (mean ± 1 standard deviation) for deep breathing and combined deep breathing and hand motion activities. Method 3 performed almost as well (24 ± 18%, 15 ± 17%), provided that adequate training data were used, and Method 2 gave a similar result (21 ± 16%, 19 ± 17%). Conclusion: This study confirms that all of the investigated methods can be used in retrospective image correction. In terms of image quality, Method 1 had a small advantage, whereas the FP-based methods measured the B0 field slightly more accurately. The specific strengths and weaknesses of FPs and navigators should therefore be considered when determining which B0-estimation method to use. Magn Reson Med 78:1373–1382, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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Wezel, J., Boer, V. O., van der Velden, T. A., Webb, A. G., Klomp, D. W. J., Versluis, M. J., … Garpebring, A. (2017). A comparison of navigators, snap-shot field monitoring, and probe-based field model training for correcting B0-induced artifacts in T2*-weighted images at 7 T. Magnetic Resonance in Medicine, 78(4), 1373–1382. https://doi.org/10.1002/mrm.26524

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