Numerical investigations of anisotropic structures of red blood cell aggregates on ultrasonic backscattering
Bibliographic record
Abstract
Although quantitative ultrasound techniques based on the parameterization of the backscatter coefficient (BSC) have been successfully applied to blood characterization, theoretical scattering models assume blood as an isotropic scattering medium. However, the red blood cell (RBC) aggregates form anisotropic structures such as rouleaux. The present study proposes an anisotropic formulation of the effective medium theory combined with the local monodisperse approximation (EMTLMA) that considers perfectly aligned prolate-shaped aggregates. Theoretical BSC predictions were first compared with computer simulations of BSCs in a forward problem framework. Computer simulations were conducted for perfectly aligned prolate-shaped aggregates and more complex configurations with partially aligned prolate-shaped aggregates for which the size and orientation of RBC aggregates were obtained from blood optical observations. The isotropic and anisotropic EMTLMA models were then compared in an inverse problem framework to estimate blindly the structural parameters of RBC aggregates from the simulated BSCs. When considering the isotropic EMTLMA, the use of averaged BSCs over different insonification directions significantly improves the estimation of aggregate structural parameters. Overall, the anisotropic EMTLMA was found to be superior to the isotropic EMTLMA in estimating the scatterer volume distribution. These results contribute to a better interpretation of scatterer size estimates for blood characterization.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".