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 delay-corrected 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. HighlightsO_LIThis work investigates the feasibility of using a one-time system calibration to account for k-space trajectory deviations in spiral fMRI. C_LIO_LIThis versatile calibration is based on a linear time-invariant gradient model, the gradient impulse response function (GIRF). C_LIO_LIWe show that the image quality and the spatial specificity of the fMRI activation are substantially improved when using the GIRF-predicted trajectories. C_LIO_LIBasing reconstructions on nominal gradient inputs, on the other hand, induces image artifacts and misplaced fMRI activation. C_LIO_LIWe demonstrate that system characterization via the GIRF can enable spiral fMRI in situations where concurrent trajectory monitoring is unavailable. C_LI