Servo navigation and phase equalization enhanced by run-time stabilization (PEERS) for 3D EPI time series

TL;DR

This work tackles motion and B0 fluctuations in 3D EPI fMRI by introducing PEERS, a plug-and-play framework that fuses run-time servo navigation with data-driven retrospective phase equalization. Servo navigation provides on-line motion and bulk frequency corrections via a short 3D orbital navigator and a real-time linear model, while PEERS exploits the repetitive EPI time-series structure to perform shot-wise phase/frequency corrections relative to peer shots. The phantom and in-vivo results demonstrate that the combination yields substantial stability gains, with tSNR improvements up to about 60% in unaligned data and around 12% after realignment, especially under motion. The approach reduces sensitivity to intra- and inter-volume motion and field fluctuations, offering a robust, automatic solution for improving 3D EPI fMRI data quality in practical settings.

Abstract

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 as well as bulk phase and frequency fluctuation. 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 strategy is evaluated 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. In turn, retrospective phase equalization is found to eliminate shot-wise phase and frequency offsets relative to the navigator, which are attributed to eddy-currents and vibrations from phase encoding. Retrospective phase equalization reduces the precision requirements for run-time frequency control, supporting the use of short navigators. Relative to conventional volume realignment, PEERS achieved tSNR improvements up to $30\%$ for small motion and in the order of $10\%$ when volunteers tried to hold still. Retrospective phase equalization is found to clearly outperform phase correction based solely on navigator-based frequency estimates. Conclusion: Servo navigation achieves high-precision run-time motion correction for 3D EPI fMRI. Coarse frequency tracking based on short navigators is supplemented by precise retrospective frequency and phase correction. Fully automatic and self-calibrated, PEERS offers effective plug-and-play motion and phase correction for 3D fMRI.

Figures (10)

• Figure 1: Servo navigation for segmented 3D EPI. (A) Sequence diagram. (B) Servo navigation overview. A 3D orbital navigator (Nav) with adaptable duration $T_{nav}$ is inserted between excitation (Ex) and EPI readout. After steady-state build-up, reference navigators are acquired on the fly to calibrate the linear model for motion and field parameter estimation. The parameters are used for run-time scan geometry control closing the feedback loop for updated linear parameter estimation.
• Figure 2: Phase equalization enhanced by run-time stabilization (PEERS). (A) Shots from the first EPI stack (volume) serve as a reference for phase equalization of subsequent shots after prospective motion correction (PMC). (B) For any subsequent shot from volume $d$, relative phases are calculated echo-by-echo by scalar products of each echo signal $\vec{S}_{d,l,m}$ with its respective reference peer. $m$ and $l$ are echo and slice encoding indices. (C) Phases are unwrapped along the EPI echo train ($m$) followed by a first-order fit yielding frequency (slope) and phase (offset) parameters.
• Figure 3: In-vivo example with instructed motion comparing Servo off and Servo on. (Left): Mean and standard deviation (Std) of the voxel time series. (Right): realignment parameters. For Servo off, the mean image is blurred and the std shows strong edge enhancement and choppy patterns in the logarithmic standard deviation, which are clearly mitigated by Servo on. The realignment parameters for Servo off show the instructed motion pattern, while being close to zero for Servo on due to effective run-time motion control.
• Figure 4: Volume realignment statistics for all in-vivo subjects without instructed motion comparing Servo off (black boxes) and Servo on (green boxes). Boxplots for shifts along RL, AP, and FH are shown, as well as the respective rotations. A table of parameter RMSE values is included. Motion is consistently reduced by servo navigation for all parameters. The RL shift (phase encoding direction), which includes both subject motion and EPI shifting from frequency drift, shows the strongest reduction.
• Figure 5: Phase equalization of voxel times series in a phantom scan without servo navigation. (A): Mean and standard deviations (Std) without and with phase equalization. Each image is split by half showing the image before and after realignment. (B): Phase $\Delta\phi_{\mathrm{epi}}$ and frequency ${\Delta}f_{\mathrm{epi}}=\Delta\omega_{\mathrm{epi}}/(2\pi)$ estimated by phase equalization. (C): Zoom into (B). EPI shifting from the frequency drift is reduced by realignment without phase equalization. Phase equalization captures the frequency drift well (B) and further improves volume consistency by intra-volume phase correction. Repetitive patterns with $T_{vol}$-period are visible in (C).