A new approach to shimming: The dynamically controlled adaptive current network
Bibliographic record
Abstract
PURPOSE: Magnetic field homogeneity is important in all aspects of magnetic resonance imaging. A new approach to increase field homogeneity is presented that allows dynamic and adaptive control over the flow of current over a single surface using a network of actively controlled solid-state switches. METHODS: Computer simulations were completed demonstrating the potential of this approach. Wire patterns were produced using the boundary element method to remove magnetic field inhomogeneities over multiple regions of interest. Field maps and regions of interest histograms were compared with and without the shim present. A prototype was constructed confirming the feasibility of this approach within the magnetic resonance environment. Metal-oxide-semiconductor field-effect transistors were used. Two field maps were acquired with the prototype producing gradient and offset field profiles, respectively. The experimental field profiles were compared with simulation. RESULTS: The wire patterns significantly increased field homogeneity over all regions of interest investigated. The field profiles produced by the prototype matched simulation. No imaging artifacts were produced. CONCLUSIONS: An approach to control the shape of a current distribution over a single surface has been described. This method has the potential to improve field homogeneity over any desired region of interest and is particularly well suited for dynamic applications. The method is feasible with current technology and construction techniques.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".