SMASH and SENSE: Experimental and numerical comparisons
Why this work is in the frame
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Bibliographic record
Abstract
Three parallel-imaging methods were implemented and compared in terms of artifact and noise content: original SMASH, Cartesian SENSE, and an extremely simple method called here the "scissors method." These methods represent very different approaches to the parallel-imaging problem. The experimental and numerical comparisons presented here aim at shedding light on the whole spectrum of parallel-imaging methods, not just the three methods actually implemented. In our results, SMASH images had an artifact level significantly higher than SENSE images for all acceleration factors. The SNR in SENSE images was nearly optimal at low acceleration factors. As acceleration was increased, the noise content in SENSE images eventually sharply departed from optimal values, while the artifact content remained low.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it