An orthogonal shim coil for 3T brain imaging
Bibliographic record
Abstract
PURPOSE: We designed and implemented an orthogonal shim array consisting of shim coils with their planes perpendicular to the planes of neighboring RF coils. This shim coil improves the magnetic field homogeneity by minimizing the interference to RF coils. METHODS: Using realistic off-resonance maps of the human brain, we first evaluated the performance of shim coils in different orientations. Based on simulations, we developed a 7-channel orthogonal shim array, whose coil plan was perpendicular to neighboring RF coils, at the forehead. A programmable open-source current driver supplied shim currents. RESULTS: The 7-channel orthogonal shim array caused only marginal SNR loss to the integrated 32-channel RF-shim array. The 7-channel orthogonal shim array itself improved the magnetic field homogeneity by 30% in slice-optimized shimming, comparable to the baseline shimming offered by the scanner's 2nd order spherical harmonic shimming. CONCLUSION: Orthogonal shim coils can improve the field homogeneity while maintaining high image SNR.
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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 | 0.001 |
| 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) | 0.003 | 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".