P3E-3 Finite Element Modeling of Ultrasound Scattering by Spherical Objects and Cells
Bibliographic record
Abstract
It has been shown that high-frequency ultrasound (20 MHz-60 MHz) can be used to detect structural and physical changes in tissues and cell ensembles during cell death. However, the changes observed are not well understood. Recent theoretical models treating the cell as a homogeneous sphere did not show good agreement with experimental data. We have recently developed a finite element model of wave propagation through inhomogeneous spherical structures (COMSOL Multiphysics, COMSOL Inc., Burlington, MA) to solve the problem of high-frequency acoustic scattering from cells. We will discuss the improvement to our previous model by using a second-order boundary condition. The improved model predicts scattering by a homogeneous sphere with a 2% average accuracy when compared to the Faran model. Applications of the model to ultrasound scattering by two types of objects that represent biological cells will be presented. In both applications the cell is a sphere (representing the nucleus) surrounded by a spherical shell (representing the cytoplasm). The cytoplasm has either an elastic property with a stiffness less than that of the nucleus, or is fluid, having similar physical properties to water. The deformations of an air-filled microbubble subject to various acoustic wave frequencies will also be presented. Finally, the significance of this work on the prediction of ultrasound backscatter from cells, and on ultrasound tissue characterization techniques will be discussed
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".