Evaluation of multicoil breast arrays for parallel imaging
Bibliographic record
Abstract
PURPOSE: To evaluate three multicoil breast arrays for both conventional and SENSE-accelerated imaging. MATERIALS AND METHODS: Two commercially available 8-element coils and a prototype 16-element coil were compared. One 8-element array had adjustable coils located next to the breast tissue and the other had a fixed coil arrangement; both were designed to allow parallel imaging in the left-right direction. The 16-element coil was designed to have coil sensitivity variation in both the left-right and superior-inferior directions, and also had adjustable coils. Their performance was assessed in terms of signal-to-noise ratio (SNR), g-factor, and uniformity with a custom-built phantom. RESULTS: The 16-element array with adjustable coils provided the highest SNR, while the 8-element coil with a fixed coil arrangement had the best uniformity. All coils performed well for SENSE acceleration in the left-right direction. The 8-element coils did not have the capability for acceleration in the superior-inferior direction across the whole volume. The 16-element coil enabled acceleration in the superior-inferior direction in addition to the left-right direction. CONCLUSION: Smaller, adjustable coil elements located next to breast tissue can provide greater SNR than larger, fixed coil elements. A multicoil breast array with high intrinsic SNR and low g-factors enables high-quality parallel imaging.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".