Size-Selected Microbubbles for Superharmonic Contrast Imaging
Bibliographic record
Abstract
The nonlinear behavior of microbubbles (MBs) is dependent on both the excitation pressure and the MB properties, which rely on the composition of the gas core and shell, as well as the size. Polydisperse MBs have a broad size distribution and only a subpopulation of them may be contributing to the higher order harmonics that are used for superharmonic imaging. High MB concentrations have been used for superharmonic imaging to produce nonlinear responses from the polydisperse MB solution for good image contrast. In this work, we investigate in vitro the contrast signal intensity and longivity with in-house polydisperse MBs and size-selected MBs of ~1.5, 2.2, and 4.3 µm in diameter, comparing to commonly used MicroMarker MB solutions. We found the in-house 2.2 µm MBs showed comparable mean contrast intensities and signal decay to MicroMarker. We evaluated these two populations in vivo on mouse kidneys over an approximately 18-minute imaging duration, and assessed quantitatively the contrast intensities and longevity of superharmonic signals. We found MicroMarker showed greater superharmonic contrast over the entire acquisition in vivo, with a half-life almost twice to that of the in-house 2.2 µm MBs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".