A source wavelet deconvolution approach to improve the spatial resolution for radar-based breast imaging system
Bibliographic record
Abstract
In microwave breast imaging, image reconstruction involves tomographic reconstruction, radar-based imaging, or some combination of both methods (J. F. Deprez, et al., PIER B, 42, 381-403, 2012). With radar-based techniques, similar basic processing steps, i.e., calibration and skin subtraction are taken to process the raw data prior to their deployment in Kirchhoff summation (image focusing). Usually, minimal consideration is given to improve the temporal resolution of the raw signals. However, the spatial resolution associated with the reconstructed image is a function of the temporal resolution of raw data. With simple breast phantoms, i.e., homogeneous breast volume with a single inclusion, an image with low spatial resolution may give a relatively accurate estimate of inclusion location, however lacks sensitivities to the changes in its size. With complicated breast phantoms, i.e., heterogeneous breast volume with multiple malignant and benign inclusions, low imaging resolution may blur malignant inclusions into adjacent features. Inspired by seismic imaging data processing workflow, we propose the method of source wavelet deconvolution for pre-processing prior to Kirchhoff summation. The idea is to improve the temporal resolution of calibrated raw data by compressing the source wavelet. We also propose the method of picking the first break (arrival) of wave propagation for the purpose of locating the scatterer in the signal. Previously, the signals were integrated prior to Kirchhoff summation, which assumes that a local maximum indicates the round-trip distance between the antenna and the scatterer. In reality, this assumption is not always true due to the complexity of wave propagation (A. Ishimaru, 1997). In this sense, the proposed new processing workflow can be described as improved calibration, skin subtraction, deconvolution, and reconstruction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".