Servo Navigation and Phase Equalization Enhanced by Run-Time Stabilization (PEERS) for 3D EPI Time Series.

PURPOSE: To enhance time-resolved segmented imaging by synergy of run-time stabilization and retrospective data-driven phase correction. METHODS: A segmented 3D EPI sequence for fMRI time series is equipped with servo navigation based on short orbital navigators and a linear perturbation model, enabling run-time correction for rigid-body motion, bulk phase, and frequency fluctuations. Complementary retrospective phase correction is based on the repetitive structure of the time series and serves to address residual phase and frequency offsets. The combined approach is termed phase equalization enhanced by run-time stabilization (PEERS). RESULTS: The proposed methods are assessed in terms of resulting alignment and tSNR of 3D EPI time series in a phantom and in vivo. Servo navigation is found to diminish motion confound in raw data and maintain k-space consistency over time series. Coarse frequency tracking based on short navigators is supplemented by precise shot-wise frequency and phase correction using retrospective phase equalization of EPI data. Relative to conventional volume realignment, PEERS achieved tSNR improvements of up to 30 % for small motion and on the order of 10 % when volunteers tried to hold still. CONCLUSION: Servo navigation achieves high-precision run-time motion correction for 3D EPI and is synergistic with retrospective phase equalization. Fully automatic and self-calibrated, PEERS offers effective plug-and-play motion and phase correction for stabilized 3D EPI time series. The translation of stability gains into metrics of functional sensitivity is complex and must be studied with suitable statistical power. The data obtained in this work does not support any conclusions in this regard.