Inter-shot Motion Correction of Segmented 3D-GRASE ASL Perfusion Imaging with Self-Navigation and CAIPI
Bibliographic record
Abstract
Abstract 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 that ensure full k-space coverage were explored for point spread function (PSF) 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 their impact on motion robustness in five healthy volunteers with instructed head motion. Results Our method effectively reduced motion artifacts and outperformed conventional inter-volume correction by 12.3% in correlation coefficient, 4.5% in Structural Similarity Index Measure (SSIM), 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 performance. The tag-control (T/C) inner loop acquisition yielded better motion robustness across all 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".