Predictive Model for Designing Soft-Tissue Mimicking Ultrasound Phantoms With Adjustable Elasticity
Bibliographic record
Abstract
The use of mechanically representative phantoms is important for experimental validation in ultrasound (US) imaging, elastography, and image registration. This article proposes a model to predict the elastic modulus of a soft tissue-mimicking phantom based on two very easily controllable parameters: gelatin concentration and refrigeration duration. The model has been validated on small- and large-scale phantoms; it provides a good prediction of the elastic modulus in both cases (error < 16.2%). The tissue-mimicking phantom is made following a low-cost and simple fabrication procedure using commercial household gelatin with psyllium hydrophilic mucilloid fiber to obtain echogenicity. A large range of elastic properties was obtained (15-100kPa) by adjusting the gelatin concentration between 5% and 20% (g/mL) and the refrigeration time of the sample between 2 and 168 h, allowing to mimic normal and pathological human soft tissues. The phantom's acoustic properties (velocity, attenuation, and acoustic impedance) are also assessed using the American Institute of Ultrasound in Medicine (AIUM) standard.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".