Prospective motion correction for R2* and susceptibility mapping using spherical navigators
Bibliographic record
Abstract
Abstract Purpose To perform prospective motion correction (PMC) for improved and susceptibility mapping using a purely navigator‐based approach. Methods Spherical navigators (SNAVs) were combined with an additional FID readout for simultaneous measurement of motion and zeroth‐order field shifts. The resulting FIDSNAVs were interleaved for PMC of a multi‐echo gradient echo sequence with retrospective correction. Experiments were performed on a 3T scanner with a 32‐channel head coil. Performance was assessed in five volunteers with motion prompts derived from real unintentional motion trajectories. Results At short TEs, PMC alone was sufficient to achieve good image quality; at longer TEs, retrospective correction was often just as important for artifact reduction as motion correction. Both PMC and retrospective correction reduced error in and susceptibility maps for all participants. Residual artifacts were observed in the most severe motion case. Conclusion Combining SNAVs with an additional FID readout enables simultaneous motion and field correction with no additional hardware requirements, improving the fidelity of quantitative mapping in the presence of motion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".