Real-time clinically oriented array-based in vivo combined photoacoustic and power Doppler imaging
Bibliographic record
Abstract
Photoacoustic imaging has great potential for identifying vascular regions for clinical imaging. In addition to assessing angiogenesis in cancers, there are many other disease processes that result in increased vascularity that present novel targets for photoacoustic imaging. Doppler imaging can provide good localization of large vessels, but poor imaging of small or low flow speed vessels and is susceptible to motion artifacts. Photoacoustic imaging can provide visualization of small vessels, but due to the filtering effects of ultrasound transducers, only shows the edges of large vessels. Thus, we have combined photoacoustic imaging with ultrasound power Doppler to provide contrast agent- free vascular imaging. We use a research-oriented ultrasound array system to provide interlaced ultrasound, Doppler, and photoacoustic imaging. This system features realtime display of all three modalities with adjustable persistence, rejection, and compression. For ease of use in a clinical setting, display of each mode can be disabled. We verify the ability of this system to identify vessels with varying flow speeds using receiver operating characteristic curves, and find that as flow speed falls, photoacoustic imaging becomes a much better method for identifying blood vessels. We also present several in vivo images of the thyroid and several synovial joints to assess the practicality of this imaging for clinical applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".