Simplified skipped phase encoding and edge deghosting (SPEED) for imaging sparse objects with applications to MRA
Bibliographic record
Abstract
The fast imaging method named skipped phase encoding and edge deghosting (SPEED) has been demonstrated to reduce scan time considerably with typical magnetic resonance imaging data. In this work, SPEED is simplified with improved efficiency to accelerate the scan of sparse objects; we refer to this method as S-SPEED. S-SPEED partially samples k-space into two interleaved data sets, each with the same skip size of N but a different relative shift in phase encoding. The sampled data are then Fourier transformed into two ghosted images with N aliasing ghosts. Given the sparseness of signal distribution, the ghosted images are simply modeled with a single-layer structure, analogous to that used in maximum-intensity projection. With an algorithm based on a least-square-error solution, a deghosted image is solved, and a residual map is output for quality control. S-SPEED can be generalized to include more layers with additional acquisitions for refined results. Without differential filtering and full central k-space sampling, S-SPEED reduces scan time further and achieves more straightforward reconstruction, as compared with SPEED. In this work, S-SPEED is applied to accelerate magnetic resonance angiography (MRA) by taking advantage of the sparse nature of MRA data. With sparse phantom data and in vivo phase contrast MRA data, S-SPEED is demonstrated to achieve satisfactory results with an acceleration factor of 5.5 using a single coil.
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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 | 0.000 |
| 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".