Robust automated shimming technique using arbitrary mapping acquisition parameters (RASTAMAP)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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