Algebraic method to synthesize specified modal currents in ladder resonators: Application to noncircular birdcage coils
Bibliographic record
Abstract
PURPOSE: Detectors such as birdcage coils often consist of networks of coupled resonant circuits that must produce specified magnetic field distributions. In many cases, such as quadrature asymmetric insert body coils, calculating the capacitance values required to achieve specified currents and frequencies simultaneously is a challenging task that previously had only approximate or computationally inefficient solutions. THEORY AND METHODS: A general algebraic method was developed that is applicable to linear networks having planar representations such as birdcage coils, transverse electromagnetic (TEM) coils, and numerous variants of ladder networks. Unlike previous iterative or approximate methods, the algebraic method is computationally efficient and determines current distribution and resonant frequency using a single matrix inversion. The method was demonstrated by specifying irregular current distributions on a highly elliptical birdcage coil at 3 Tesla. RESULTS: Measurements of the modal frequency spectrum and transmit field distribution of the two specified modes agrees with the theory. Accuracy is limited in practice only by how accurately the matrix of self and mutual inductances of the network is known. CONCLUSION: The algebraic method overcomes the inability of the existing inductance equalization method to account for all elements of the inductance matrix and the inability to accommodate modal currents that are not (co)sinusoidal.
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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 | 0.000 | 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".