Optimizing spherical navigator echoes for three‐dimensional rigid‐body motion detection
Bibliographic record
Abstract
Spherical navigator (SNAV) echoes show promise in correcting for three-dimensional rigid-body motion. In this paper, several important parameters in the design and performance of the SNAV technique are discussed, including a novel sampling strategy, the optimal k-space radius and sampling density of the navigator, and the execution of the SNAV trajectory by the scanner hardware. A variable-sampling density (VSD) helical-spiral SNAV trajectory, which can acquire data on the entire spherical shell without exceeding the maximum slew rate of the scanner, is presented. To ensure that the VSD SNAV trajectory was properly executed by the scanner hardware, the gradient waveforms were verified using a self-encoding technique. The ability of the VSD SNAV to measure rotational and translational motion was studied with in vitro experiments at various k-space radii and sampling densities. The results of this study show that the best accuracy was attained at k-space radii of 1.4 and 1.6 cm(-1), with 2400 to 4000 samples acquired over the sphere.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".