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Purpose
In this work, a new method to determine the gradient system transfer function (GSTF) with high frequency resolution and high SNR is presented, using fast and simple phantom measurements. The GSTF is an effective instrument for hardware characterization and calibration, which can be used to correct for gradient distortions, or enhance gradient fidelity.
Methods
The thin‐slice approach for phantom‐based measurements of the GSTF is expanded by adding excitations that are shifted after the application of the probing gradient, to capture long‐lasting field fluctuations with high SNR. A physics‐informed regularization procedure is implemented to derive high‐quality transfer functions from a small number of measurements. The resulting GSTFs are evaluated by means of gradient time‐course estimation and pre‐emphasis of a trapezoidal test gradient on a 7T scanner.
Results
The GSTFs determined with the proposed method capture sharp mechanical resonances with a high level of detail. The measured trapezoidal gradient progressions are authentically reproduced by the GSTF estimations on all three axes. The GSTF‐based pre‐emphasis considerably improves the gradient fidelity in the plateau phase of the test gradient and almost completely eliminates lingering field oscillations.
Conclusion
The presented approach allows fast and simple characterization of gradient field fluctuations caused by long‐living eddy current and vibration effects, which become more pronounced at ultrahigh field strengths.
Eddy current induced field perturbations can cause artifacts in diffusion weighted imaging (DWI). Such perturbations may be calculated and corrected for by the gradient system transfer function (GSTF), provided the gradient system behaves linear and time-invariant. We discovered that the linearity assumption can be violated by gradients with high zeroth order moments. We therefore propose a modified measurement scheme for the GSTF including higher gradient moments to better approximate their behavior. Our approach substantially reduces errors in the predicted gradient time courses. We may thus enable the application of GSTF-based correction methods to DWI in the future.
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