Photoacoustic imaging for ultrasound tissue characterization and treatment monitoring
Bibliographic record
Abstract
Photoacoustic imaging relies on generation of ultrasound waves from optically absorbing structures. The ultrasound produced in photoacoustic imaging can be analyzed by methods developed to analyze ultrasound backscatter signals for ultrasound tissue characterization, but the interpretation of the analysis is based on the physics of photoacoustic wave generation. In the absence of exogenous absorbers, blood is one of the dominant optically absorbing tissues. Hemoglobin in red blood cells is the main endogenous chromophore in blood. The spatial distribution of red blood cells in tissue determines the spectral characteristics of the ultrasound signals produced. We are interested in cancer treatment monitoring. Tumor blood vessels have distinct organizational structure compared to normal blood vessels: normal vessel networks are hierarchically organized, with vessels that are evenly distributed to ensure adequate oxygen and nutrient delivery. Tumor vessels are structurally different: they are torturous and hyperpermeable. Therapies that target the vasculature can induce changes in the vascular networks that in principle should be detected using photoacoustic imaging. In this work, we will show how the frequency content of the photoacoustic signals encodes information about the size, concentration, and spatial distribution of non-resolvable blood vessels that can be used to assess treatment response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".