Bibliographic record
Abstract
The SiMultaneous Acquisition of Spatial Harmonics (SMASH) technique uses linear combinations of undersampled datasets from the component coils of an RF coil array to reconstruct fully sampled composite datasets in reduced imaging times. In previously reported implementations, SMASH reconstructions were designed to reproduce the images that would otherwise be obtained by simple sums of fully gradient encoded component coil images. This strategy has left SMASH images vulnerable to phase cancellation artifacts when the sensitivities of RF coil array elements are not suitably phase-aligned. In fully gradient encoded imaging schemes these artifacts can be eliminated using a variety of methods for combining the individual coil images, including matched filter combinations as well as sum of squares combinations. Until now, these reconstruction schemes have been unavailable to SMASH reconstructions as SMASH produced a final composite image directly from the raw component coil k-space datasets. This article demonstrates a modification to SMASH that allows reconstruction of a full set of accelerated individual component coil images by fitting component coil sensitivity functions to a complete set of spatial harmonics tailored for each coil in the array. Standard component coil combinations applied to the individual reconstructed images produce final composite images free of phase cancellation artifacts.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".