Abstract
Purpose 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 and 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.
Methods GIRF-predicted reconstruction was tested for high-resolution (0.8mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the delay corrected nominal trajectory and concurrent field monitoring.
Results The reconstructions using nominal spiral trajectories contain substantial artifacts and activation maps contain mis-placed 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.
Conclusion 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 high-resolution fMRI in situations where concurrent monitoring is not available.
Highlights
This work investigates the feasibility of using a one-time system calibration (called GIRF) based on a linear time-invariant gradient model to account for k-space trajectory deviations in spiral fMRI.
We show that the image quality and the spatial specificity of the fMRI activation are substantially improved when using the GIRF-prediction for trajectory correction while the nominal reconstructions suffer from artifacts and mis-placed fMRI activation.
We demonstrate that system characterization via the GIRF can enable spiral fMRI in situations when concurrent monitoring is not available.