Tissue Mimicking Materials for Shell-Based Phantoms in Breast Microwave Sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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 it