Tissue Mimicking Materials for Shell-Based Phantoms in Breast Microwave Sensing
Bibliographic record
Abstract
Breast phantoms are required to test and evaluate microwave breast imaging systems before clinical applications. Shell-based breast phantoms are versatile, reproducible, low-cost, stable, and capable of mimicking the morphology and dielectric properties of the breast. In past work, 3D-printable plastics have been used to fabricate the shells in these phantoms, but the low permittivity plastics limit the dielectric accuracy of the phantoms. Furthermore, the liquids in these shell-based phantoms are prone to air bubbles, which may introduce undesirable microwave scattering. This work examines new tissue-mimicking materials to address these challenges. Low-permittivity 3D-printed plastic filament was replaced with a graphite, carbon-black, and resin mixture to mimic skin properties within the 0.4–9.0 GHz range. Glycerin and Triton X-100 were replaced by diethylene glycol butyl ether (DGBE) solutions to mimic the properties of adipose and fibroglandular tissue. The resin-based material more closely modelled the properties of ex vivo tissue samples than 3D-printed plastics. The DGBE solutions had improved dielectric properties compared to the glycerin and Triton X-100 solutions. The DGBE solutions are advantageous compared to glycerin and Triton X-100 solutions due to their lower viscosity, decreased susceptibility to air bubble formation, improved short-term stability, temperature stability, and enhanced long-term stability, facilitating the reusability of these materials. The materials investigated in this work can be used to produce more dielectrically accurate breast phantoms with improved stability and experimental utility for microwave breast imaging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".