High frequency nonlinear B-scan imaging of microbubble contrast agents
Bibliographic record
Abstract
It previously was shown that it is possible to produce nonlinear scattering from microbubble contrast agents using transmit frequencies in the 14-32 MHz range, suggesting the possibility of performing high-frequency, nonlinear microbubble imaging. In this study, we describe the development of nonlinear microbubble B-scan imaging instrumentation capable of operating at transmit center frequencies between 10 and 50 MHz. The system underwent validation experiments using transmit frequencies of 20 and 30 MHz. Agent characterization experiments demonstrate the presence of nonlinear scattering for the conditions used in this study. Using wall-less vessel phantoms, nonlinear B-scan imaging is performed using energy in one of the subharmonic, ultraharmonic, and second harmonic frequency regions for transmit frequencies of 20 and 30 MHz. Both subharmonic and ultraharmonic imaging modes achieved suppression of tissue signals to below the noise floor while achieving contrast to noise ratios of up to 26 and 17 dB, respectively. The performance of second harmonic imaging was compromised by nonlinear propagation and offered no significant contrast improvement over fundamental mode imaging. In vivo experiments using the subharmonic of a 20 MHz transmit pulse show the successful detection of microvessels in the rabbit ear and in the mouse heart. The results of this study demonstrate the feasibility of nonlinear microbubble imaging at high frequencies.
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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) | 0.001 | 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".