Effect of Injection Parameters on the MRI and Dielectric Properties of Condensation-Cured Silicone
Bibliographic record
Abstract
Phantoms with tissue-mimicking properties play a crucial role in the calibration of medical imaging modalities, including Magnetic Resonance Imaging (MRI). Among these phantoms, silicone-based ones are widely used due to their long-term stability in MR imaging. Most of these phantoms are manufactured using traditional pour-mold techniques which often result in the production of air bubbles that can damage the phantom. This research investigates the feasibility of utilizing extrusion techniques to fabricate silicone phantoms and explores the effects of extrusion parameters including plunger speed and nozzle diameter on void content, T1 and T2 relaxation times, and dielectric properties. A custom double-syringe silicone extrusion apparatus was developed to prepare the silicone samples. The void content, relaxometry, and dielectric properties of extruded samples were measured and compared with traditional poured samples. The results show that extrusion parameters can affect the void content of the silicone samples. The presence of voids in the samples resulted in lower T1 values, indicating an inverse relationship between void content and relaxometry. This study demonstrates the potential of extrusion techniques for manufacturing silicone phantoms with reduced air bubble formation and provides valuable insights into the relationship between extrusion parameters and phantom properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".