Decorrelation Time Mapping as an Analysis Tool for Nanobubble-Based Contrast Enhanced Ultrasound Imaging
Bibliographic record
Abstract
Abstract Nanobubbles (NBs) are nanoscale (∼100-500 nm diameter) ultrasound (US) contrast agents that enable new robust applications of contrast enhanced US and US-mediated therapy. Due to their sub-micron size, high particle density, and highly deformable shell, NBs exhibit unique properties. In pathological states of heightened vascular permeability, such as in tumours, NBs can extravasate, enabling extravascular applications not currently possible with clinically available microbubbles (∼1000-10,000 nm diameter). This ability can be explored to develop imaging biomarkers to improve tumour detection. There is a need for an imaging method that can rapidly and effectively separate intravascular versus extravascular NB signal when imaged using nonlinear dynamic contrast enhanced US. Herein, we demonstrated the use of decorrelation time (DT) mapping to achieve this goal. Two in vitro models were used to explore the roles of NB velocity and diffusion on DTs. Mice bearing prostate specific membrane antigen (PSMA) expressing flank tumours (n = 7) were injected with bubble agents to evaluate the in vivo potential of this technique. The DT was calculated at each pixel of nonlinear contrast videos to produce DT maps. Across all models, long DT correlated with slowly moving or entrapped NBs while short DT correlated with flowing NBs. DT maps were sensitive to NBs in tumour tissue with high average DT in tumour regions (∼10 s) compared to surrounding normal tissue (∼1 s). Molecular NB targeting to PSMA extended DT (17 s) compared to non-targeted NBs (12 s), demonstrating sensitivity to NB adherence dynamics. Overall, DT mapping of in vivo NB dynamics produced detailed information of tumour tissue and showed potential for quantifying extravascular NB kinetics. This new NB-contrast enhanced US-based biomarker can be useful in molecular ultrasound imaging, with improved sensitivity and specificity of target tissue detection and potential for use as a predictor of vascular permeability and the enhanced permeability and retention (EPR) effect in tumours.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".