Fast measurement of the gradient system transfer function at 7 T.

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.

The temperature dependence of gradient system response characteristics.

PURPOSE: The gradient system transfer function (GSTF) characterizes the frequency transfer behavior of a dynamic gradient system and can be used to correct non-Cartesian k-space trajectories. This study analyzes the impact of the gradient coil temperature of a 3T scanner on the GSTF. METHODS: GSTF self- and B0 -cross-terms were acquired for a 3T Siemens scanner (Siemens Healthcare, Erlangen, Germany) using a phantom-based measurement technique. The GSTF terms were measured for various temperature states up to 45°C. The gradient coil temperatures were measured continuously utilizing 12 temperature sensors which are integrated by the vendor. Different modeling approaches were applied and compared. RESULTS: The self-terms depend linearly on temperature, whereas the B0 -cross-term does not. Effects induced by thermal variation are negligible for the phase response. The self-terms are best represented by a linear model including the three gradient coil sensors that showed the maximum temperature dependence for the three axes. The use of time derivatives of the temperature did not lead to an improvement of the model. The B0 -cross-terms can be modeled by a convolution model which considers coil-specific heat transportation. CONCLUSION: The temperature dependency of the GSTF was analyzed for a 3T Siemens scanner. The self- and B0 -cross-terms can be modeled using a linear and convolution modeling approach based on the three main temperature sensor elements.

Gradient waveform pre-emphasis based on the gradient system transfer function.

PURPOSE: The gradient system transfer function (GSTF) has been used to describe the distorted k-space trajectory for image reconstruction. The purpose of this work was to use the GSTF to determine the pre-emphasis for an undistorted gradient output and intended k-space trajectory. METHODS: The GSTF of the MR system was determined using only standard MR hardware without special equipment such as field probes or a field camera. The GSTF was used for trajectory prediction in image reconstruction and for a gradient waveform pre-emphasis. As test sequences, a gradient-echo sequence with phase-encoding gradient modulation and a gradient-echo sequence with a spiral read-out trajectory were implemented and subsequently applied on a structural phantom and in vivo head measurements. RESULTS: Image artifacts were successfully suppressed by applying the GSTF-based pre-emphasis. Equivalent results are achieved with images acquired using GSTF-based post-correction of the trajectory as a part of image reconstruction. In contrast, the pre-emphasis approach allows reconstruction using the initially intended trajectory. CONCLUSION: The artifact suppression shown for two sequences demonstrates that the GSTF can serve for a novel pre-emphasis. A pre-emphasis based on the GSTF information can be applied to any arbitrary sequence type.

Field camera versus phantom-based measurement of the gradient system transfer function (GSTF) with dwell time compensation.

PURPOSE: The gradient system transfer function (GSTF) can be used to describe the dynamic gradient system and applied for trajectory correction in non-Cartesian MRI. This study compares the field camera and the phantom-based methods to measure the GSTF and implements a compensation for the difference in measurement dwell time. METHODS: The self-term GSTFs of a MR system were determined with two approaches: 1) using a dynamic field camera and 2) using a spherical phantom-based measurement with standard MR hardware. The phantom-based GSTF was convolved with a box function to compensate for the dwell time dependence of the measurement. The field camera and phantom-based GSTFs were used for trajectory prediction during retrospective image reconstruction of 3D wave-CAIPI phantom images. RESULTS: Differences in the GSTF magnitude response were observed between the two measurement methods. For the wave-CAIPI sequence, this led to deviations in the GSTF predicted trajectories of 4% compared to measured trajectories, and residual distortions in the reconstructed phantom images generated with the phantom-based GSTF. Following dwell-time compensation, deviations in the GSTF magnitudes, GSTF-predicted trajectories, and resulting image artifacts were eliminated (< 0.5% deviation in trajectories). CONCLUSION: With dwell time compensation, both the field camera and the phantom-based GSTF self-terms show negligible deviations and lead to strong artifact reduction when they are used for trajectory correction in image reconstruction.