A homogeneous RF-shielded magnet for low-field magnetic resonance studies
Bibliographic record
Abstract
Today’s clinical magnetic resonance imaging (MRI) systems have B0-fields of 1.5 − 7.0T, and research usage time on these high-field systems is limited and expensive. The development of novel MRI techniques, such as transmit array spatial encoding (TRASE), can be accelerated by access to in-house, small-scale, low-field systems. This thesis therefore develops a method for designing a low-field magnet with excellent field homogeneity. The value of this method is its versatility: while it is used here to design a uniform field within a cylindrical volume, the method can also be applied to other geometries and to produce any desired field profile. An overview of the design method, and the research performed in this thesis, is as follows. The magnetic scalar potential Φ is chosen inside what will be the magnet volume to give the desired field profile; outside, however, an outer boundary surface is set-up and Φ is solved for numerically from the Laplace equation using a finite element method within the region between the two surfaces. The discontinuity in the magnetic scalar potential ∆Φ at the surface of the inner volume gives the required surface current distribution of the magnet, which is then discretized into wires. Biot-Savart field calculations are used to simulate the field and quantify its homogeneity. The cylindrical magnet of 25-cm radius and 1-m length designed here is found to have a theoretical homogeneity of < 1 ppm over a 20-cm diameter spherical volume.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".