Spectrum optimization for x-ray dual-mode imager comprising radiography and coherent scatter
Bibliographic record
Abstract
Medical, industrial and security x-ray systems should give images that demonstrate material contrast for accurate identification. Acquiring images simultaneously from coherently scattered x-rays plus primary is an efficient way to do so. In our projection imaging system for small biological samples, the same detector is used for both primary and scatter, necessitating attenuation of the post-object primary in order that both primary and scatter lie within the detector’s dynamic range. Consideration of the cross sections shows that the peak scatter-to-primary fluence ratio is nearly independent of photon energy. At lower energies, coherent scatter produces larger diffraction rings, which give better spatial separation of primary and scatter, but there is more attenuation and multibeam information disentanglement is more difficult. Previous development work for our system used a 110kV incident beam, with a 1.5-mm-thick post-object attenuator disk of 90% W/10% Cu alloy for each of the 15 pencil beams. In this work we investigate alternatives which are less attenuating. By reducing the kV and using K-edge filters the beam average energy is lowered to achieve greater primary image contrast. Preliminary primary beam results are shown.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".