Benjamin Emanuel Dietrich^{1}, Jennifer Nussbaum^{1}, Bertram Jakob Wilm^{1}, Jonas Reber^{1}, and Klaas Paul Pruessmann^{1}

Under the assumption that a gradient system is linear and time-invariant (LTI), accurate gradient field waveforms can be predicted by gradient response functions. However, time-invariance can be violated due to heating of system components. Temperature sensors can be used to assess heating of the gradient coils. To assess the predictability of gradient response function based on temperature measurements, the temperature dependence of gradient response functions is analyzed using an NMR probe based field camera and optically connected temperature sensors. From this data a prediction model is generated and tested for its application in image reconstruction.

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Temperature
profiles and response function measurement points during the calibration and
test sessions for various heating scenarios. The yellow shaded areas indicate
heating periods and the gradient waveforms (Static: trapezoidal pulses
(80 % duty cycle), Sine: 500 Hz sinusoids of 100 ms duration
(80 % duty cycle)) and input channels that were used to heat up the system.

Magnitude
(a) and deviation (b) plots of all measured response functions (all
temperatures) for each gradient channel. The close-up of the y-channel
magnitude plot shows a, most likely mechanical, resonance of the gradient coil.

Response
function prediction results (a) and corresponding prediction errors (b) for all
3 gradients. The main readout frequency of the used EPI readout (x-gradient) is
indicated by a green arrow.

EPI
trajectories, calculated with the different test response functions, and the
nominal (ideal) input trajectory.

EPI
reconstruction based on simulated data with trajectories calculated from
different response functions.