Bibliographic record
Abstract
PURPOSE: A readout-segmented method for acquiring robust GRAPPA calibration data for echo-planar imaging (EPI) was proposed and compared with two previous methods, including the gold standard interleaved approach and a single shot method with halved phase encode resolution. THEORY AND METHODS: The readout-segmented and single shot techniques acquire adjacent phase encode lines in the same shot to obtain the calibration data, rather than interleaving of lines between shots, to decrease sensitivity to motion. Additionally, it uses multiple segments with shortened frequency encode extent to match the phase encode bandwidth to the undersampled data, which decreases sensitivity to B0 inhomogeneity. The three methods were tested using simulations and EPI scans of the brain in healthy volunteers. RESULTS: The interleaved approach exhibited high sensitivity to motion, while residual undersampling artifacts remained in the single shot method due to mismatch of B0 inhomogeneity between the calibration and undersampled data. The readout segmented method exhibited no such errors, having 30% lower ghosting intensity than the single shot method and 90% lower ghosting intensity than the interleaved approach in moving subjects. CONCLUSION: Artifacts from B0 inhomogeneity and motion during calibration scans for EPI GRAPPA can be mitigated with a readout segmented calibration scan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".