Polyvinyl alcohol cryogels (PVA-C): preparation and multimodal imaging applications
Bibliographic record
Abstract
Polyvinyl alcohol cryogel (PVA-C) is a widely used tissue-mimicking material in medical imaging phantoms, essential for evaluating imaging techniques, training clinicians, and simulating anatomical tissue structures. Its tunable mechanical and imaging properties make it valuable for multimodal imaging. However, PVA-C phantom reproducibility remains challenging due to fabrication variations, including inconsistencies in freeze-thaw cycle (FTC) conditions, PVA concentration, and imaging additives. This study systematically examines how PVA concentration, FTC profiles, and contrast-enhancing additives affect the mechanical and imaging properties of PVA-C phantoms in ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT). Using in-house experiments and a comprehensive literature review, we analyze FTC conditions’ role in modulating stiffness, attenuation, and relaxation times. We also assess contrast agents such as barium sulfate, gadolinium, and silica-based scatterers on imaging visibility and result. Our findings identify optimal fabrication conditions that enhance PVA-C phantom reproducibility and performance across imaging modalities. Our findings offer a comprehensive framework for optimizing PVA-C phantoms, ensuring reliable multimodal imaging applications.
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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 | 0.001 | 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 | 0.000 | 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 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".