Three-dimensional computational modeling of high-frequency ultrasound imaging of murine liver and liver metastases.
Bibliographic record
Abstract
High-frequency ultrasound images of preclinical cancer models are sensitive to variations in microanatomy that accompany tumor growth, but the relationships between high-frequency backscattering and tumor microstructure are incompletely understood. To investigate these relationships, a 3-D microanatomical model that incorporates cell number density, sizes of cells and nuclei, and spatial arrangement of cells is used to represent a healthy mouse liver and an experimental liver metastasis. A first-order k-space method is used to synthesize B-mode images by computing linear 3-D propagation of focused 40-MHz pulses through the simulated tissues. Simulated images are compared to corresponding experimental images by constructing gray-level histograms with 13 bins evenly spaced over 256 gray levels. Simulated and experimental speckle distributions for a healthy liver match within one standard deviation in all 13 histogram bins when the sound speed and mass density of the cell nuclei are set to 1503 m/s and 1.43 g/cm3, respectively. Simulated and experimental speckle distributions for the liver metastasis match in 11 of 13 bins when the sound speed and density of the nuclei are set to 1527 m/s and 1.14 g/cm3. These simulations suggest that changes in first-order speckle statistics during tumor development reflect variations in both tissue acoustic and microstructural properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".