A study of contrast-enhanced functional microwave imaging
Bibliographic record
Abstract
Microwave imaging (MWI) continues to develop as a low-cost portable complementary soft-tissue imaging modality, particularly in the context of breast cancer detection. Despite dramatic advancements in algorithmic development and signal acquisition, even the most recent imaging studies have shown that challenges still remain to improve this emerging technology's spatial and contrast resolution for anatomical imaging. However, MWI's ability to detect unique properties dependent on the physiological state of a tissue of interest at non-ionizing frequency ranges suggest it may be suitable for safer, cheaper functional imaging studies using non-radioactive contrast agents. This study explores applications in niches traditionally filled by nuclear medicine, where contrast-enhanced MWI could achieve resolutions comparable to existing imaging procedures but with no associated radiation dose. Non-toxic compounds that exhibit strong microwave-band responses, notably transition metal nanoparticles (S. Semenov et al., IFMBE Proc. 25/8, 311-313, 2009) and free radicals, may have promise in such contrast-enhanced imaging to provide metabolic rather than strictly anatomic data. To fully exploit the information available from these agents, the addition of an external weak polarizing magnetic field (PMF) across the imaging domain is necessary, which primarily influences ferromagnetic or strongly paramagnetic contrast agents within that domain (O.M. Bucci et al., IEEE Trans. Biomed. Eng., 58, 9, 2528-2536, 2011). Along with the traditionally measured changes in permittivity and conductivity, the PMF allows variations in magnetic susceptibility to contribute to the relevant microwave image data through resonance phenomena (P.C. Fannin, J. Mol. Liquids, 114, 79-87, 2004).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".