Elastic Property Monitoring by Radiation Force Impulse and Phase Contrast Imaging
Bibliographic record
Abstract
In tissue engineering, one promising methodology is the scaffold based approach, where an artificial construct is seeded with cells, which then proceed to organize and proliferate into new tissue. The scaffold then biodegrades, leaving behind the newly formed tissue that originally developed in the scaffold’s pores. The degradation behavior of the scaffold is critical to its performance during the treatment period, since the decline in scaffold mechanical properties influences the loading of the tissue developing in the scaffold pores, which is known to have an effect on cell behavior. To monitor the scaffold’s mechanical properties, soft scaffolds are deformed by the acoustic radiation force generated by an ultrasound source. Measuring the deflection the scaffold experiences from this ultrasound based radiation force is challenging, since the scaffold is surrounded by the living environment. In this paper, an in-vitro methodology is presented, proceeding from scaffold fabrication, scaffold imaging, image analysis, mathematical equations, and finally model implementation. The innovation comes from the author’s use of in-line phase contrast x-ray imaging at 20 KeV to characterize tissue scaffold deformation from ultrasound radiation forces, and the measured deformation is then compared with predictions given by the forward solution of a mathematical model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".