Nanobubble Contrast Enhanced Ultrasound Imaging: A Review
Bibliographic record
Abstract
Contrast-enhanced ultrasound is currently used worldwide with clinical indications in cardiology and radiology, and it continues to evolve and develop through innovative technological advancements. Clinically utilized contrast agents for ultrasound consist of hydrophobic gas microbubbles stabilized with a biocompatible shell. These agents are used commonly in echocardiography, with emerging applications in cancer diagnosis and therapy. Microbubbles are a blood pool agent with diameters between 1 and 10 μm, which precludes their use in other extravascular applications. To expand the potential use of contrast-enhanced ultrasound beyond intravascular applications, sub-micron agents, often called nanobubbles or ultra-fine bubbles, have recently emerged as a promising tool. Combining the principles of ultrasound imaging with the unique properties of nanobubbles (high concentration and small size), recent work has established their imaging potential. Contrast-enhanced ultrasound imaging using these agents continues to gain traction, with new studies establishing novel imaging applications. We highlight the recent achievements in nonlinear nanobubble contrast imaging, including a discussion on nanobubble formulations and their acoustic characteristics. Ultrasound imaging with nanobubbles is still in its early stages, but it has shown great potential in preclinical research and animal studies. We highlight unexplored areas of research where the capabilities of nanobubbles may offer new advantages. As technology advances, this technique may find applications in various areas of medicine, including cancer detection and treatment, cardiovascular imaging, and drug delivery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".