A New Breast Phantom With a Durable Skin Layer for Microwave Breast Imaging
Bibliographic record
Abstract
Breast phantoms (BPs) are required to test and validate microwave breast imaging prototypes. For this purpose, a new BP made from carbon/rubber mixtures is proposed. These materials have: 1) electrical properties that are stable over time and representative of human target values and 2) mechanical properties that allow the material to be flexible and withstand reasonable stress. To characterize and optimize the carbon/rubber materials, samples made with varying carbon concentrations were created and the dielectric properties were measured. From these materials, a skin layer, fatty layer, glandular structures, and phantom tumors were cast from three-dimensional (3-D) printed molds and assembled into a complete BP. These phantoms mimic the anatomical structures of the breast, are reconfigurable for a variety of tests, and are easy to create in a typical lab environment. A microwave breast imaging prototype system was used to measure reflections from BPs. Comparison with reflections from human trials demonstrated that the phantom provides appropriate skin reflections. Phantoms incorporating glandular structures were imaged using a delay-and-sum technique. A response consistent with the position of the inclusions was observed. Overall, the carbon-based phantoms provide similar reflections to human tissue, and have proven useful for testing our imaging algorithms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".