Magnetic resonance imaging of ultrasound fields: Gradient characteristics
Bibliographic record
Abstract
Phase-contrast magnetic resonance imaging (MRI) is used to image particle displacements arising from a 0.515-MHZ focused ultrasound (US) field. The technique used a phase-locked, self-resonant gradient matched to the US frequency in conjunction with a spin-echo sequence to generate phase images of US-induced displacement parallel to the US propagation direction. The gradient design was numerically optimized to provide maximum linearity and magnitude while minimizing gradient inductance. The windings were fabricated of Litz wire to minimize resistive losses and mounted in an oil-cooled imaging chamber. When driven by a resonance power supply, a peak magnetic field gradient of 0.40 T/m was attained with a peak current of 20 amp in a volume of 53 cm(3), achieving stable oscillation at the required US frequency. Clear detection of the nanometer scale particle motions of the US field was achieved and allowed quantitative, noninvasive visualization of the entire US field. While the required gradient slew rate for US detection is beyond that recommended for in vivo application, this imaging method opens new possibilities for in vitro or ex vivo research in the study of the interaction of US with tissue.
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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 | 0.000 | 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".