A two-component speckle model for detection of microbubble signals in linear contrast-enhanced ultrasonography
Bibliographic record
Abstract
Contrast-enhanced ultrasound (CEUS) serves oncology by imaging tumor blood supply to enable quantification of longitudinal vascular changes and monitoring of treatment responses. Unfortunately, the linear subtraction methods commonly used for preclinical imaging are susceptible to registration errors and motion artifacts that lead to reduced contrast-to-tissue ratios. In this presentation, an alternative approach is proposed to improve discrimination between the contrast and tissue signals by comparing the first-order speckle statistics of images acquired before and after injection of microbubbles. The microbubble signal component is modeled as a temporally varying random process superimposed on a Rayleigh-distributed speckle signal representing backscatter from tissue. Images were acquired at 18 MHz from a murine orthotopic (mammary fat pad) xenograft breast cancer model following a bolus injection of microbubbles. Images were processed using gold-standard pulse inversion nonlinear CEUS, conventional linear subtraction, and the proposed statistical method. In comparison to conventional linear CEUS, the statistical method produced a wash-in curve that showed closer agreement to the gold-standard nonlinear CEUS data. The statistical method eliminates baseline image subtraction from linear CEUS processing, which should streamline the imaging workflow, improve the robustness of image quantification, and enable real-time perfusion imaging with linear CEUS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".