An improved homogeneity design method for fast field‐cycling coils in molecular MRI
Bibliographic record
Abstract
PURPOSE: insert coil to shift the magnitude of the main magnetic field as a magnetization preparation phase of the pulse sequence. Here, an improved coil design method is presented that minimizes field inhomogeneities and allows for explicit control of the ROI. METHODS: A solenoid produces the bulk field shift, and a boundary element method is employed to design in-series shim and shield layers. A design is presented and compared to the current generation dreMR coil design on field inhomogeneity maps, shield performance, and simulated dreMR image. A proof-of-concept design is also presented with an ROI shifted from isocenter. RESULTS: The new design is able to image a sphere of 8.5 cm in diameter with field inhomogeneity of < 1% versus the previous design's 5 cm. The new design presented an increase in shielding capabilities, whereas inductance and resistance increased. For a simulated dreMR image, the new design presented errors < 10% compared to an ideal field simulation, whereas the previous design had errors > 18%. The shifted ROI design produced a region of < 1% inhomogeneity much larger than a design with no shim layer. CONCLUSION: The new design method was found to greatly improve the insert coil field homogeneity and reduce errors in dreMR imaging in simulation without detriment to shielding. This method's capability to increase ROI and control its location will be used to design human dreMR coils going forward.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".