Robust automated shimming technique using arbitrary mapping acquisition parameters (RASTAMAP)
Bibliographic record
Abstract
Quantitative MRI techniques as well as methods such as blood oxygen level-dependent (BOLD) imaging and in vivo spectroscopy require stringent optimization of magnetic field homogeneity, particularly when using high main magnetic fields. Automated shimming approaches require a method of measuring the main magnetic field, B(0), followed by adjusting the currents in resistive shim coils to maximize homogeneity. A robust automated shimming technique using arbitrary mapping acquisition parameters (RASTAMAP) using a 3D multiecho gradient echo sequence that measures B(0) with high precision was developed. Inherent compensation and postprocessing methods enable removal of artifacts due to hardware timing errors, gradient propagation delays, gradient amplifier asymmetry, and eddy currents. This allows field maps to be generated for any field of view, bandwidth, resolution, or acquisition orientation without custom tuning of sequence parameters. Field maps of an aqueous phantom show +/- 1 Hz variation with altered acquisition orientations and bandwidths. Subsequent fitting of measured shim coil field maps allows calculation of shim currents to produce optimum field homogeneity.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".