12A-1 Concentration Requirements for Subharmonic Quantitative Contrast Enhanced High Frequency Ultrasound Flow Studies
Bibliographic record
Abstract
The ability to perform quantitative haemodynamic measurements in the microcirculation using high frequency ultrasound and microbubbles is dependent on the ability to suppress tissue signals and the concentration of the agent. While tissue suppression has been accomplished by exploiting nonlinear properties of microbubbles, it is still unclear as to the optimal dose to facilitate flow studies. A dose response study of the contrast Agent MicroMarker was performed in the renal cortex of a mouse to get an indication of the optimal range of doses. A transmit pulse of 30 MHz, 20% bandwidth and peak negative pressure (PNP) of 1 MPa was used to elicit a 15 MHz subharmonic response from the agent. Doses up to 300 muL kg-1, corresponding to 10 million bubbles per mL of blood, were investigated. Experiments showed a wash-in phase which took approximately 3 to 5 seconds, followed by a plateau in signal which lasted for approximately 40 to 50 seconds and a slow washout phase. The average enhancement calculated over the plateau peaked at 4.5 times (6.5 dB) over the noise floor. The duration of enhancement increased with dose up to 230 seconds. The integrated enhancement (area under the time intensity curves) was linear over the range of doses investigated. The variability between mice was 10% to 20% for doses between 20 and 100 muL kg-1. A dose of 40 % was observed for a dose of 300 muL kg-1and is believed to be due to microbubbles-induced attenuation. This study has established the utility of microbubble contrast agents for quantitative flow imaging in the microcirculation using high frequency ultrasound for doses up to 300 muL kg-1dose range. However, larger doses should be investigated in order to improve enhancement.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".