Development of an x-ray prism for analyzer based imaging systems
Bibliographic record
Abstract
Analyzer crystal based imaging techniques such as diffraction enhanced imaging (DEI) and multiple imaging radiography (MIR) utilize the Bragg peak of perfect crystal diffraction to convert angular changes into intensity changes. These x-ray techniques extend the capability of conventional radiography, which derives image contrast from absorption, by providing large intensity changes for small angle changes introduced from the x-ray beam traversing the sample. Objects that have very little absorption contrast may have considerable refraction and ultrasmall angle x-ray scattering contrast improving visualization and extending the utility of x-ray imaging. To improve on the current DEI technique an x-ray prism (XRP) was designed and included in the imaging system. The XRP allows the analyzer crystal to be aligned anywhere on the rocking curve without physically moving the analyzer from the Bragg angle. By using the XRP to set the rocking curve alignment rather than moving the analyzer crystal physically the needed angle sensitivity is changed from submicroradians for direct mechanical movement of the analyzer crystal to tens of milliradians for movement of the XRP angle. However, this improvement in angle positioning comes at the cost of absorption loss in the XRP and depends on the x-ray energy. In addition to using an XRP for crystal alignment it has the potential for scanning quickly through the entire rocking curve. This has the benefit of collecting all the required data for image reconstruction in a single measurement thereby removing some problems with motion artifacts which remain a concern in current DEI/MIR systems especially for living animals.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".