Accelerating MRI by skipped phase encoding and edge deghosting (SPEED)
Bibliographic record
Abstract
A fast imaging method called skipped phase encoding and edge deghosting (SPEED) is introduced. The k-space is sparsely sampled into three interleaved datasets, each with a skip-size N and a relative shift in phase encoding (PE). These datasets are separately reconstructed by 2DFT and edge-enhanced by a differential filter in the PE direction, resulting in edge maps with phase-shifted aliasing ghosts. The sparseness of edges reduces the chance of ghost overlapping. Typical ghosted-edge maps can be adequately modeled with only two dominating ghost layers that are resolved from a set of three equations using least-square error minimization, yielding N ghost maps of different orders that can be registered and averaged into a single deghosted-edge map for noise and artifact reduction. Finally, the deghosted-edge map is transformed into a deghosted image by an inverse filter. A few central k-space lines are collected without PE skip to aid the inverse filtering. SPEED has been demonstrated by in vivo data to reduce scan time considerably without noticeable artifacts. It has various potential applications, such as MR angiography (MRA), where the signal itself is sparse. As an independent method, SPEED can be combined with other fast imaging methods for further acceleration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".