Compound speckle model demonstrates two-phase wash-in of contrast-enhanced ultrasound cine loops
Bibliographic record
Abstract
During contrast-enhanced ultrasound (CEUS) imaging of tumor microvascular perfusion, nearby arteriole enhancement can be a dominant feature of the wash-in kinetics that obscures the perfusion characteristics of the capillary bed. This presentation demonstrates that statistical wash-in curves generated using our compound CEUS speckle model1 exhibit two distinct phases corresponding to “fast-flow” and “slow-flow” enhancement that can be detected by fitting a linear combination of two monoexponential functions with different time constants. CEUS cine loops were acquired from a patient-derived xenograft model of renal cell carcinoma, where fresh tumor fragments were engrafted into the chicken embryo chorioallantoic membrane. Subharmonic CEUS images were acquired at 18 MHz using a destruction-replenishment protocol. Enhancement of manually segmented tumor cross-sections was analyzed offline in MATLAB. The CEUS cine loops from this xenograft model frequently exhibited in-plane arteriole enhancement. The two-phase fit discriminated a slow-flow component in 26.6% of time-intensity wash-in curves and 62.9% of the statistical wash-in curves, leading to a decrease in estimated tumor blood volume by 22.1% and 24.7%, respectively, compared to a simple monoexponential fit. These results suggest that conventional CEUS processing may frequently overestimate capillary blood volume. 1M.R. Lowerison et al., Med. Phys. 44, 99-111 (2017).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".