Finite-element modeling of microsphere surface modes and high-frequency ultrasound scattering from a single cell
Bibliographic record
Abstract
A finite-element model of wave propagation using COMSOL Multiphysics (COMSOL Inc., Burlington, MA) was developed to solve the problem of high-frequency ultrasound (10–70 MHz) scattering from spheres. This model is being used to predict ultrasound backscatter from cells for ultrasound tissue characterization. In this work, backscatter from an elastic (polystyrene) microsphere was used to validate the computational model against experimental results. Agreement between experimental results and finite-element solutions were found in the frequency domain solutions. Moreover, conversion of the theoretical solutions to time-domain backscatter data showed good agreement with experimental time domain measurements. To develop a better understanding of the interactions of the incident pressure field with the scattering object, oscillations of the elastic microsphere at various resonance frequencies were also investigated. A systematic relationship between the resonance frequency and its corresponding surface mode was found. An ultrasound scattering model by a single cell is also presented. The model treats the cell as an elastic sphere (nucleus) surrounded by a fluid shell (cytoplasm). Comparison of the theoretical backscatter predicted by the model and experimental measurements for acute Myeloid Leukemia (AML) cell is also shown. Finally, the implications of these results on ultrasound tissue characterization techniques are discussed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".