A Short and Light, Sparse Dipolar Halbach Magnet for MRI
Bibliographic record
Abstract
Recently designed dipolar Halbach magnets used in portable MRI systems are much lighter and more compact than standard permanent or superconductive magnets. However, improved designs and manufacturing techniques aiming at lower weight and smaller external size are an area of continual interest especially for application to space flight. Most Halbach magnet design techniques aim to optimize homogeneity suitable for MRI over a diameter-spherical volume (DSV) that requires the aspect ratio (length/inner diameter) to be larger than 1.5:1. Furthermore, current magnet construction techniques often use low-coercivity magnetic pieces and imperfect formers that produce a mismatch in the intended designs. As a result, Halbach magnets require complex shimming methods to improve the magnetic field homogeneity, causing further size and weight increase. Here, we propose to reduce the weight and the aspect ratio of the Halbach magnet by optimizing homogeneity over a cylindrical region of interest (ROI) rather than a DSV, applying a genetic algorithm, high-coercivity ferromagnets (N40UH) and a robust construction technique. The assembled 67 mT magnet, with aspect ratio ~ 1:1, produces almost identical homogeneity (11152 ppm) as simulations (11451 ppm) within a 12.7 cm diameter, 1 cm long cylinder ROI. The magnet structure was 3D printed ring-by-ring and assembled coaxially. The magnet can be disassembled for transportation.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".