A geometrically decoupled, twisted solenoid single‐axis gradient coil set for TRASE
Bibliographic record
Abstract
Purpose TRASE uses phase gradients in the RF transmit field to encode MRI data. A highly efficient twisted solenoid coil was proposed recently for TRASE imaging for transverse geometries. This novel coil can be rotated to generate a phase gradient in any transverse direction, therefore, combining two such coils would double k‐space coverage for single‐axis encoding, resulting in higher spatial resolution. However, the strong inductive coupling between a pair of coaxial twisted solenoids must be overcome. Methods Here, we demonstrate that two concentric twisted solenoids, designed using previously described Biot‐Savart calculations, can be geometrically decoupled by attaching to each a regular solenoid in series. The regular solenoid geometry resulting in minimization of mutual inductance was determined from simulations using the FastHenry2 tool. The effects on TRASE encoding performance due to the regular solenoids were assessed from simulations and experiments. Results The maximum resulting magnitude and phase distortions were 3.7% and , while a good isolation dB between the coil pair was obtained. TRASE experiments confirmed the double k‐space coverage, and achieved a rapid spin echo train with 128 k‐space points collected within 80 ms, allowing short samples to be accurately imaged. Conclusions This study demonstrates that a pair of twisted solenoid phase gradient RF coils can be geometrically decoupled. Advantages over active PIN diode decoupling include faster switching, lower hardware complexity, and scalability.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".