Design and Optimization of Gradient Coils for Low-field Halbach Array Scanners Using the Discrete Wire Method
Bibliographic record
Abstract
Halbach array magnets are usually preferred for low-field, portable, point-of-care scanners due to their ability to generate a more homogeneous magnetic field. An efficient gradient coil design for Halbach array scanners is desired to get high-quality magnetic resonance (MR) images. However, there is still a challenge in obtaining desirable linearity and efficiency at the target diameter of spherical volume (DSV) for axial gradient coils using target field methods. This work aims to investigate the discrete wire approach to design efficient gradient coils for Halbach array scanners. The coil turns of each quadrant of both transverse and axial gradient coils were parameterized using quasi-elliptic functions. The gradient coils are then optimized to maximize the coil's efficiency while keeping the linearity error and the maximum field deviation less than 10% and lower than 5%, respectively. The coil geometric parameters, current, turn locations, center of a quadrant, and quasi-elliptic parameters are used as the design variables. Results showed that the designed Y, X, and Z (axial), gradient coils could achieve 2.84 mT/m/A, 2.40 mT/m/A, and 1.21 mT/m/A, respectively over a cylindrical volume with a length of 6 cm and a diameter of 6 cm. This method will be further investigated to design gradient coils for diffusion-weighted imaging (DWI) applications at low field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".