Intervention‐based multidimensional phase unwrapping using recursive orthogonal referring
Bibliographic record
Abstract
We present a new intervention-based phase unwrapping algorithm, which solves the inherent integration-path-dependent problem (typically resulting in streaks), by using a 2D recursive orthogonal referring (PUROR) approach. The streaks were removed by three consecutive procedures: intra-image phase unwrapping, inter-image cross-referring a "good-strip," and cross-referring line segments. The application of these procedures results in streak-free 2D phase images. The phase inconsistencies across slices in a 3D image were removed using a hybrid 3D PUROR algorithm: the two step approach involves stacking the individual slices, by using the mean phase values of each slice, then applying the 2D PUROR algorithm to reformatted 2D images that include the slice direction. The described approach was tested with in vivo multislice phase images acquired in the axial, sagittal, and coronal orientation. The results of the unwrapped phase volume recovered using the PUROR algorithm have equivalent quality to that achieved by using established methods, but the PUROR algorithm is about two orders of magnitude faster (between 1 and 5 s per 256×256 slice; independent of slice orientation and echo time).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".