Image‐based method to measure and characterize shim‐induced eddy current fields
Bibliographic record
Abstract
ABSTRACT Dynamic magnetic field shimming is gaining interest for field sensitive MRI acquisitions. Using slice based or real‐time shim updating, significant improvements in static field (B0) uniformity can be obtained. While the ability to rapidly switch shim fields can improve overall B0 homogeneity, it induces eddy current fields that must be characterized and compensated for. Methods used to achieve this have thus far been based on linear projection spin echo sequences or field probe assemblies. Here, a novel image‐based method is presented to measure and characterize eddy current fields without the need for field probes or projection based measurements. This technique can be extended to characterize very high order spherical harmonic fields, making it a useful tool to calibrate next‐generation shim systems implementing dynamic field steering with greater than third order shim terms. Results are used to calibrate a Dynamic Shim Updating unit for pre‐emphasis and eddy current compensation. Three‐dimensional datasets are acquired at multiple MR facilities containing complete spatiotemporal field information to compensate eddy current field self‐ and cross‐terms for up to third order. Furthermore, simulation studies are performed to investigate the effect of scan resolution and phantom size with respect to accurate eddy current field characterization. © 2014 Wiley Periodicals, Inc. Concepts Magn Reson Part A 42A: 245–260, 2013.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".