New Resolution Enhancement Approach for Tissue Sensitive Adaptive Radar (TSAR)
Bibliographic record
Abstract
Tissue sensitive adaptive radar (TSAR) is a non-ionizing, near-field radar imaging technique proposed for the long-term monitoring of breast cancer. Microwave techniques inherently have a lower resolution than MRI or X-ray making it important that the TSAR image reconstruction algorithm does not unintentionally introduce further resolution loss. The 3D TSAR image is reconstructed by summing the intensity and voxel-location information encoded in the multiple 1D round-trip time-delay signals reflected from breast features. Decoding requires knowledge of system and patient properties. We have identified differences in the highest intensity voxel location present in TSAR images after summing all decoded antenna time-domain data streams compared to summing a few localized data streams. This potentially can lead to image resolution loss. We propose approaches to determine whether these differences must be accepted as a limit of the current technology and software or whether they can be systematically removed by making minor, but experimentally relevant, empirical changes in system or patient properties. We identified that the consistency improvements in simulated and patient data identified in the empirical study could mainly be accounted for by calculating the transit time between the antenna aperture and feed using AR modeling rather than the current DFT based techniques.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".