Efficient multiple acquisitions by skipped phase encoding and edge deghosting (SPEED) using shared spatial information
Bibliographic record
Abstract
The fast MRI method of Skipped Phase Encoding and Edge Deghosting (SPEED) is further developed to accelerate multiple acquisitions. In a single acquisition, SPEED first acquires three sparse ghosted edge maps with an undersampling factor of N/3, which are modeled with a double-layer structure and described by three equations with two unknown ghosts, each with a unique ghost order index. By minimizing least-square-error, a pair of ghost order indexes is determined. Based on them, the two corresponding ghosts are resolved, leading to a deghosted image. In this case, three equations are needed to determine the ghost order index, while only two equations are required to resolve the two ghosts. This shows both inefficiency and potential. Multiple acquisitions often contain similar spatial information. The similarities can be used to improve efficiency by sharing the ghost order index among different acquisitions, leading to acceleration factors greater than that achievable with single acquisition.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".