Modeling strategies of ultrasound backscattering by blood
Bibliographic record
Abstract
Tissue characterization using ultrasound (US) scattering can allow the identification of relevant cellular biophysical information noninvasively. The characterization of the level of red blood cell (RBC) aggregation is one of the proposed applications. Different modeling strategies have been investigated by our group to better understand the mechanisms of US backscattering by blood, and to propose relevant measurable indices of aggregation. It could be hypothesized from these studies that the microstructure formed by RBC clusters is a main determinant of US backscattered power. The structure factor, which is related to the Fourier transform of the microscopic density function of RBCs, is described and used to explain the scattering behavior for different spatial arrangements of nonaggregated and aggregated RBCs. The microscopic density function was described by the Percus–Yevick approximation (nonaggregated RBCs), and for aggregated RBCs, by the Poisson distribution, the Neyman–Scott point process, and very recently by a flow-dependent rheological model. These statistical and microrheological models allowed the study of US backscattered power as a function of the hematocrit, scatterers’ size, insonification frequency, and level of RBC aggregation. Experimental results available from the literature were used to validate the different approaches. [Work supported by Canadian Institutes of Health Research (MOP-36467), HSFQ, FCAR, and FRSQ.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".