Breast cancer imaging using microwave tomography with radar-derived prior information
Bibliographic record
Abstract
Summary form only given. Biomedical imaging at microwave frequencies has shown potential for breast cancer detection and monitoring. Current modalities suffer from significant underlying disadvantages. For example, mammography utilizes high-energy, ionizing radiation and is uncomfortable for patients, and breast MRI has a high false positive rate due to high sensitivity and low specificity. Microwave imaging is an inexpensive technique that uses low-power, non-ionizing radiation that is not harmful to patients. Two techniques that exploit microwave frequencies for breast imaging are microwave tomography (MWT) and radar-based imaging. These two techniques suffer from limitations in resolution of fine structures and accuracy of tissue dielectric properties.We present a novel algorithm that combines MWT with a radar-based region estimation technique, with a focus on breast cancer imaging. The region estimation method creates a patient-specific spatial map of the breast anatomy that includes skin, adipose and fibroglandular tissue regions, and contains the average dielectric properties over those regions (D. Kurrant and E. Fear, Inverse Prob., 2012). This map is incorporated into a finite element contrast source inversion (FEM-CSI) algorithm as prior information in the form of an inhomogeneous background (A. Zakaria, A. Baran, and J. LoVetri, Antennas Wireless Propag. Lett., 2012). This hybrid approach is able to reconstruct finer structural details of tissues within the breast, and estimates their dielectric properties more accurately than either technique used alone. Results from various numerical phantoms characterize this significant improvement in image quality. In addition to improvement of accuracy and resolution, the algorithm is able to produce reliable results within the 1GHz-4GHz frequency range, allowing us to take advantage of march-on-frequency techniques to further improve image quality and localize tumors. Simulations also produce reliable results using several different immersion media that vary greatly in their real and imaginary permittivity values, providing more flexibility in the choice of immersion medium for clinical systems. Results from numerical breast phantoms using this hybrid technique will be presented and compared to traditional MWT methods.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".