Rapid six‐degree‐of‐freedom motion detection using prerotated baseline spherical navigator echoes
Bibliographic record
Abstract
A new spherical navigator echo (SNAV) registration technique is presented. This technique starts by collecting a set of SNAV templates at a reference position. These templates are acquired by rotating the gradient system to result in rotation angles that uniformly cover a predefined range of rotation. The rotation angles between an unknown physically transformed position and the reference position are subsequently determined by finding the template with the lowest sum of squared differences with SNAV at the transformed position. Translations are calculated from the phase differences between the best-match SNAV template and the SNAV acquired at the transformed position. In comparison with the conventional SNAV registration technique, the proposed technique is noniterative, robust, and can detect 3-dimensional rigid body motion in less than 50 msec. The technique was verified with phantom and in vivo experiments, which demonstrated subdegree rotational and submillimeter translational accuracy over a range of simultaneous ±20° and ±10° mm 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".