A new high frequency microultrasound system with applications in cardiovascular research.
Bibliographic record
Abstract
The development of preclinical imaging using micro-MR, CT, PET, SPECT, optical, and ultrasound technologies has created a new paradigm for imaging in the laboratories of biomedical researchers. Once considered a luxury for isolated multiuser centers, microimaging platforms are now becoming mainstream in bioresearch where quantitative in vivo imaging measurements of biomarkers and other endpoints are becoming a requirement of these investigations. Microultrasound has come a long way from its inception in the mid-1990s. This paper will detail the progression of preclinical microultrasound from mechanical to array based imaging systems. The technology of high frequency array based ultrasound imaging will be reviewed including details on the transducers and beamformer used in the first commercially available system. Applications of this system in the areas of cancer and cardiovascular disease will be described. The development of high frequency microbubble contrast modes based on linear and nonlinear signal processing will be discussed with relevant examples including imaging of VEGFR-2 and CD31 expression in disease models. All experiments with animals were done under a protocol approved by the Sunnybrook or VisualSonics Animal Care Committees. [The author declares a significant financial interest in VisualSonics Inc.]
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".