Simultaneous Use of the Born and Rytov Approximations in Real-Time Imaging With Fourier-Space Scattered Power Mapping
Bibliographic record
Abstract
The Fourier-space scattered-power mapping (F-SPM) is proposed as a computationally efficient alternative to the original real-space SPM method for real-time quantitative image reconstruction with an emphasis on close-range and near-field applications. Similar to SPM, F-SPM can employ either the Born or the Rytov data approximations in a linear scattering model, resulting in two distinct algorithms. However, to exploit the complementarity of the two approximations, a strategy is proposed for their combined use in a single inversion process. The combined Born–Rytov F-SPM method consistently improves the image quality in comparison with the images generated when using either approximation separately. The improvement is most significant when the limitations of either the Born or the Rytov approximations are violated. In the cases where neither or both of these limitations are violated, the images are of comparable quality to those generated by the standalone algorithms. The proposed Born–Rytov F-SPM algorithm is verified and compared to the standalone Born-based F-SPM and Rytov-based F-SPM in examples utilizing simulated and measured data. The gain in computational speed compared to the original real-space SPM is also demonstrated.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".