Incorporation of Ultrasonic Prior Information for Improving Quantitative Microwave Imaging of Breast
Bibliographic record
Abstract
Structural information derived via ultrasound is utilized as prior information for quantitative microwave imaging. The structural information is extracted from ray-based ultrasound reconstructions using a K-means clustering algorithm and consists of three tissue regions (skin, adipose, and fibroglandular). Tissue-specific complex permittivity values are assigned to each region (i.e., the complex permittivity is homogeneous over each region). The regions are then incorporated as an inhomogeneous numerical background in a quantitative microwave imaging algorithm (contrast source inversion). This new approach is assessed using synthetic data obtained from several anthropomorphic breast models of various densities derived from magnetic resonance imaging breast images, all containing tumors. Imaging results are quantitatively evaluated based on the algorithm's ability to detect the tumors. The performance is tested with four different variations of the prior information: two variations of the structural information and two of the assigned permittivity values. The resulting ultrasound-microwave multimodality imaging approach substantially improves the fidelity and accuracy of the reconstructed internal structures relative to previous studies that used radar-based microwave techniques to extract the internal structural information. An improvement in the sensitivity of the imaging algorithm to malignant tissue is also observed.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".