Radial TRASE: <scp>2D RF</scp> encoding through mechanical rotation and active digital decoupling
Bibliographic record
Abstract
Abstract Purpose Two‐dimensional (2D) transmit array spatial encoding (TRASE) previously required four radiofrequency fields; however, interactions between transmit (Tx) array elements caused significant challenges for 2D imaging. Here, we present a low‐cost, 2D radial encoding scheme (Radial TRASE) using a simplified two‐coil array. Theory and Methods The system consists of two B 1 phase gradient coils capable of encoding any one transverse direction. By incremental mechanical rotation over a 90° range, the encoding axis can be changed, allowing a complete radial k‐space acquisition. As a first demonstration, a wrist‐sized coil pair was experimentally verified on a 2‐MHz Halbach magnet, incorporating a static B 0 slice‐selection gradient. Although a high level of isolation is achievable geometrically, for a more robust implementation, we demonstrate the capability of active digital decoupling in eliminating residual coupling through a parallel‐transmit system. Results Radial TRASE–encoded images of water phantoms were acquired, achieving a resolution better than 1.67 mm. Rotation of the Tx array was performed during the recovery period, which caused no imaging delays. All acquired images show minimal distortions, indicating the advantage of the simplified Tx array. The active digital decoupling technique is demonstrated to eliminate residual coupled currents, effectively increasing the isolation of the two‐coil array to −50 dB. Sequential axial slice images were demonstrated using a uniform B 0 coil to shift the slice position. Conclusion Two‐coil Radial TRASE can encode a 2D slice without rapidly switched B 0 gradients. Compared with previous three‐coil or four‐coil Cartesian TRASE, the design and isolation of the Tx array are significantly simplified.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".