Superelliptical insert gradient coil with a field‐modifying layer for breast imaging
Bibliographic record
Abstract
Many MRI applications such as dynamic contrast-enhanced MRI of the breast require high spatial and temporal resolution and can benefit from improved gradient performance, e.g., increased gradient strength and reduced gradient rise time. The improved gradient performance required to achieve high spatial and temporal resolution for this application may be achieved by using local insert gradients specifically designed for a target anatomy. Current flat gradient systems cannot create an imaging volume large enough to accommodate both breasts; further, their gradient fields are not homogeneous, dropping off rapidly with distance from the gradient coil surface. To attain an imaging volume adequate for bilateral breast MRI, a planar local gradient system design has been modified into a superellipse shape, creating homogeneous gradient volumes that are 182% (Gx), 57% (Gy), and 75% (Gz) wider (left/right direction) than those of the corresponding standard planar gradient. Adding an additional field-modifying gradient winding results in an additional improvement of the homogeneous gradient field near the gradient coil surface over the already enlarged homogeneous gradient volumes of the superelliptical gradients (67%, 89%, and 214% for Gx, Gy, and Gz respectively). A prototype y-gradient insert has been built to demonstrate imaging and implementation characteristics of the superellipse gradient in a 3 T MRI system.
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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) | 0.000 | 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 | 0.001 | 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".