High-Frequency Array-Based Nanobubble Nonlinear Imaging in a Phantom and <i>In Vivo</i>
Bibliographic record
Abstract
There has been growing interest in nanobubbles (NBs) for vascular and extravascular ultrasound contrast imaging and therapeutic applications. Studies to date have generally utilized low frequencies (109mL-1), and uncalibrated B-mode or contrast-mode on commercial systems without reporting investigations on NB signatures upon which the imaging protocols should be based. We recently demonstrated that low concentrations (106mL-1) of porphyrin-lipid-encapsulated NBs scatter nonlinearly at low (2.5, 8 MHz) and high (12.5, 25, 30 MHz) frequencies in a pressure threshold-dependent manner that is advantageous for amplitude modulation (AM) imaging. Here, we implement pressure-calibrated AM at high frequency on a commercial preclinical array system to enhance sensitivity to nonlinear scattering of three phospholipid-based NB formulations. With this approach, improvements in contrast to tissue ratio relative to B-mode between 12.4 and 22.8 dB are demonstrated in a tissue-mimicking phantom, and between 6.7 and 14.8 dB in vivo.
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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 | 0.000 |
| 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 | 0.001 |
| 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".