Homogenous fat suppression for bilateral breast imaging using independent shims
Bibliographic record
Abstract
PURPOSE: To demonstrate the capability of incorporating independent shims into a dual-band spectral-spatial excitation and to compare fat suppression between standard global shims and independent shims for in vivo bilateral breast imaging at 1.5T. METHODS: A dual-band spectral-spatial excitation pulse was designed by interleaving two flyback spectral-spatial pulses, playing one during positive gradient lobes and the other during negative gradient lobes. Each slab was enabled to have an independent spatial offset, spectral offset, and slab-phase modulation by modulating radiofrequency phase, and independent linear shims were incorporated by playing extra shim gradients. Phantom experiments were performed to demonstrate the functionality of the pulse, and in vivo experiments were performed for 10 healthy volunteers to compare fat suppression between standard shims and independent shims. RESULTS: The phantom experiments confirmed that the dual-band pulse can provide independent spectral and spatial offsets and linear shims to the two slabs. Independent shims provided qualitatively more homogeneous fat suppression than standard shims in seven out of 10 subjects, with equivalent fat suppression in two of the other three subjects. CONCLUSION: Incorporating independent shims into the dual-band spectral-spatial excitation can provide homogeneous fat suppression in bilateral breast 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".