Modeling the frequency dependence (5–120 MHz) of ultrasound backscattering by red cell aggregates in shear flow at a normal hematocrit
Bibliographic record
Abstract
The frequency dependence of the ultrasound signal backscattered by blood in shear flow was studied using a simulation model. The ultrasound backscattered signal was computed with a linear model that considers the characteristics of the ultrasound system and tissue acoustic properties. The tissue scattering properties were related to the position and shape of the red blood cells (RBCs). A 2D microrheological model simulated the RBC dynamics in a Couette shear flow system. This iterative model, described earlier [Biophys. J. 82, 1696-1710 (2002)], integrates the hydrodynamic effect of the flow, as well as adhesive and repulsive forces between RBCs. RBC aggregation was simulated at 40% hematocrit and shear rates of 0.05-2 s(-1). The RBC aggregate sizes ranged, on average, from 3.3 RBCs at 2 s(-1) to 33.5 cells at 0.05 s(-1). The ultrasound backscattered power was studied at frequencies between 5-120 MHz and insonification angles between 0-180 degrees. At frequencies below approximately 30 MHz, the ultrasound backscattered power increased as the shear rate was decreased and the size of the aggregates was raised. A totally different scattering behavior was noted above 30 MHz. Typical spectral slopes of the backscattered power (log-log scale) between 5-25 MHz equaled 3.8, whereas slopes down to 0.6 were measured at 0.05 s(-1), between 40-60 MHz. The ultrasound backscattered power was shown to be angle dependent at low frequencies (5-25 MHz). The anisotropy persisted at high frequencies (>25 MHz) for small aggregates (at 2 s(-1)). In conclusion, this study sheds some light on the blood backscattering behavior with an emphasis on the non-Rayleigh regime. Additional experimental studies may be necessary to validate the simulation results, and to fully understand the relation between the ultrasound backscattered power, level of RBC aggregation, shear rate, frequency, and insonification angle.
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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 | 0.000 |
| 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".