Design study to investigate the effect of curvature on gradient coil performance for localized regions of interest
Bibliographic record
Abstract
Abstract In this study, a boundary element method has been implemented to design and analyze the performance of curved gradient coil geometries as a function of the degree of curvature over all three axes, with designs varying continuously from planar to full cylindrical. It was found that there is a monotonic increase in gradient performance with degree of curvature with little gain beyond half‐cylindrical coil geometries for a specific region of interest located 10 cm above the coil surface. The efficiencies of the half‐cylindrical geometry coils are 0.76 mT m−1 A−1, 0.71 mT m−1 A−1, and 0.76 mT m−1 A−1 for the x‐, y‐, and z‐gradient axes, respectively, when scaled to 800 μH inductance. The gradient coils presented in this study would serve as anatomically specific gradient channels to be used in conjunction with larger, whole‐body coils to comprise a 6‐channel hybrid system. The function of these channels could include the ability to provide very high performance diffusion tensor imaging in a specified volume of tissue such as the breast, prostate, or posterior regions of the brain. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 41B: 62–71, 2012
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".