Purpose Segmented 3D Gradient and Spin Echo (GRASE) is commonly used in Arterial Spin Labeling (ASL) perfusion imaging. However, it is vulnerable to inter-shot motion, leading to subtraction errors that cannot be corrected. We developed a retrospective self-navigated inter-shot motion correction method for segmented 3D-GRASE ASL imaging with Controlled Aliasing in Parallel Imaging (CAIPI).Methods Multiple shots, each uniformly covering k-space at distinct sample locations, allow a self-navigator image to be reconstructed using SENSE for each shot. Rigid-body motion estimation across the self-navigators is incorporated into a motion-compensated forward model for image reconstruction. To support self-navigation, two CAIPI-sampled segmented 3D-GRASE trajectories ensuring full k-space coverage were explored for point spread function profiles and g-factor effects. Our approach was evaluated against conventional inter-volume registration and a previously proposed method, alignedSENSE. Additionally, we compared tag-control interleaving strategies to assess the impact on motion robustness in five healthy volunteers with instructed head motion.Results With instructed moderate head motion, our method effectively reduced motion artifacts and outperformed conventional inter-volume correction by 12.3% in Pearson correlation coefficient, 4.5% in Structural Similarity Index Measure, and 40.1% in temporal SNR. It matched alignedSENSE performance while requiring only 20% of the computational time. All evaluated CAIPI sampling variants enabled robust motion correction, although tradeoffs were observed between through-plane blurring and SNR. The tag-control (T/C) inner loop acquisition yielded better motion robustness across quantitative metrics.Conclusion Self-navigated inter-shot motion correction using CAIPI sampling and a T/C inner loop for segmented 3D-GRASE ASL can improve image quality and motion robustness.
Hu et al. (Sun,) studied this question.