High Frequency Ultrasound for the Visualization and Quantification of the Microcirculation
Bibliographic record
Abstract
Advances in high‐frequency (15–80 MHz) ultrasound‐based methods for the noninvasive assessment of the microcirculation are described. Well‐established Doppler imaging approaches for vascular imaging are reviewed and their limitations discussed. The use of microbubble (MB) contrast agents with both linear and nonlinear imaging sequences are shown to extend the range of Doppler approaches to the true capillary microcirculation. In particular, nonlinear scattering by MB contrast agents provide a unique intravascular signature that can be distinguished from the echoes caused by surrounding tissues. Ultrasound (US) has the ability to selectively eliminate the contrast by momentarily increasing US power. Reflow of new contrast then allows local measurement of the microcirculation at reduced power. The characteristic “wash‐in” of MB contrast contains valuable information on the local perfusion and the blood volume of the tissue. Thus, MB contrast agents act as a tracer revealing the kinetics of tissue blood flow. Examples of wash‐in kinetics for tumor models are presented to illustrate the value of this approach for research in angiogenesis. Further refinement of this approach is described in which hemodynamic measures are mapped on a pixel‐by‐pixel basis to create parametric maps of relative blood volume and perfusion. The strengths and weaknesses of these new methods are discussed and the potential for their use in preclinical animal drug studies, clinical drug trials, and prognostic studies are described.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".