Fast 3D Breast Imaging With a Transmission-Based Microwave System
Bibliographic record
Abstract
Microwave breast imaging has recently been explored for tumor detection, treatment monitoring, and estimating breast density. Only one prior work has presented quantitative three-dimensional (3D) breast imaging based on a full-wave inverse scattering approach applied to experimental data collected from human subjects; most other works rely on quantitative 2D images or qualitative reconstructions. This paper introduces a fast and efficient 3D quantitative reconstruction approach for microwave breast imaging without the need for prior information or iterative algorithms typically used in solving full-wave equations. The method assumes wave propagation in straight lines, similar to the ray tracing method used in ultrasound imaging, and formulates the algorithm based on this assumption. The algorithm is applied to data collected at multiple antennas over a wideband frequency range with a novel microwave transmission system. This system is designed to be in direct contact with the breast, eliminating the need for a matching medium. We experimentally demonstrate quantitative 3D permittivity reconstruction for graphite phantoms with various sizes and numbers of inclusions, comparing the results with available 3D CT scans of these phantoms. Next, we test this algorithm for 3D quantitative permittivity reconstruction in four healthy participants with different breast density categories and compare the images with their mammograms. Finally, the stability of the 3D permittivity reconstruction over three time points for the participants is demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".