Mass density images from the diffraction enhanced imaging technique
Bibliographic record
Abstract
Conventional x-ray radiography measures the projected x-ray attenuation of an object. It requires attenuation differences to obtain contrast of embedded features. In general, the best absorption contrast is obtained at x-ray energies where the absorption is high, meaning a high absorbed dose. Diffraction-enhanced imaging (DEI) derives contrast from absorption, refraction, and extinction. The refraction angle image of DEI visualizes the spatial gradient of the projected electron density of the object. The projected electron density often correlates well with the projected mass density and projected absorption in soft-tissue imaging, yet the mass density is not an "energy"-dependent property of the object, as is the case of absorption. This simple difference can lead to imaging with less x-ray exposure or dose. In addition, the mass density image can be directly compared (i.e., a signal-to-noise comparison) with conventional radiography. We present the method of obtaining the mass density image, the results of experiments in which comparisons are made with radiography, and an application of the method to breast cancer imaging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".