High frequency subharmonic imaging: practical implementations and recent developments
Bibliographic record
Abstract
High-frequency ultrasound systems have been developed to provide appropriate imaging resolution for small anatomical structures. Typical applications include small animal preclinical research as well as intravascular ultrasound (IVUS) in larger animals and humans. In addition to traditional B-Mode (structural) and Doppler (functional) imaging, contrast detection modes have been implemented on these systems to improve the sensitivity to microbubbles in the microcirculation. This talk will overview some examples of these implementations and their respective advantages and disadvantages. Examples include direct radio frequency (RF) filtering and pulse sequence approaches, on both single element and array based systems. Additionally, subharmonic imaging will be compared to other harmonic detection approaches (namely nonlinear fundamental and second harmonic) and cases where subharmonic only detection is optimal at high frequencies. Recent advances explore the use of the self-demodulation phenomena to enhance to contribution of the subharmonic signal from microbubbles and improve detection. Examples of in vivo data from mice and rats will be shown, illustrating the ability to detect changes in blood perfusion by analyzing contrast uptake over time with curve fitting algorithms. Finally, the detection of microbubbles targeted to endothelial cells, using subharmonic imaging in small animals, will be demonstrated.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".