Thermal variation in gradient response: measurement and modeling.

PURPOSE: Many aspects and imperfections of gradient dynamics in MRI have been successfully captured by linear time-invariant (LTI) models. Changes in gradient behavior due to heating, however, violate time invariance. The goal of this work is to study such changes at the level of transfer functions and model them by thermal extension of the LTI framework. METHODS: To study the impact of gradient heating on transfer functions, a clinical MR system was heated using a range of high-amplitude DC and AC waveforms, each followed by measuring transfer functions in rapid succession while the system cooled down. Simultaneously, gradient temperature was monitored with an array of temperature sensors positioned according to initial infrared recordings of the gradient tube. The relation between temperatures and transfer functions is cast into local and global linear models. The models are analysed in terms of self-consistency, conditioning, and prediction performance. RESULTS: Pronounced thermal effects are observed in the time resolved transfer functions, largely attributable to in-coil eddy currents and mechanical resonances. Thermal modeling is found to capture these effects well. The keys to good model performance are well-placed temperature sensors and suitable training data. CONCLUSION: Heating changes gradient response, violating time invariance. The utility of LTI modeling can nevertheless be recovered by a linear thermal extension, relying on temperature sensing and adequate one-time training.

Feasibility of spiral fMRI based on an LTI gradient model.

Spiral imaging is very well suited for functional MRI, however its use has been limited by the fact that artifacts caused by gradient imperfections and B0 inhomogeneity are more difficult to correct compared to EPI. Effective correction requires accurate knowledge of the traversed k-space trajectory. With the goal of making spiral fMRI more accessible, we have evaluated image reconstruction using trajectories predicted by the gradient impulse response function (GIRF), which can be determined in a one-time calibration step. GIRF-predicted reconstruction was tested for high-resolution (0.8 mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the nominal trajectory and concurrent field monitoring. The reconstructions using nominal spiral trajectories contain substantial artifacts and the activation maps contain misplaced activation. Image artifacts are substantially reduced when using the GIRF-predicted reconstruction, and the activation maps for the GIRF-predicted and monitored reconstructions largely overlap. The GIRF reconstruction provides a large increase in the spatial specificity of the activation compared to the nominal reconstruction. The GIRF-reconstruction generates image quality and fMRI results similar to using a concurrently monitored trajectory. The presented approach does not prolong or complicate the fMRI acquisition. Using GIRF-predicted trajectories has the potential to enable high-quality spiral fMRI in situations where concurrent trajectory monitoring is not available.

Ultrafast Ligand Self-Exchanging Gadolinium Complexes in Ionic Liquids for NMR Field Probes.

Concurrent magnetic field monitoring in MRI with an array of NMR field probes allows for reducing image imperfections. High 19F concentrations together with short relaxation times are basic probe properties. We present the NMR properties of [Gd(NTf2)4]- dissolved in an ionic liquid consisting of an imidazolium cation and the [NTf2]- anion. These solutions achieve fluorine concentrations as high as 26 M and rapid relaxation times in the sub-ms time range. The second order self-exchange rate of coordinated [NTf2]- with [NTf2]- as determined with [Y(NTf2)4]- is 4.5 × 105·M-1·s-1. X-ray structure analyses confirm the coordination number of eight around Gd3+ and Y3+. These ionic-liquid solutions are thus excellent candidates for dynamically probing the NMR fields in MRI.