Bibliographic record
Abstract
Purpose: To create projection x-ray images by capturing pencil-beam scattered radiation patterns step-by-step. Each scatter profile is integrated to generate an image of scatter cross section versus position. Such a system could improve x-ray contrast or reduce dose using information currently discarded in radiological images to augment the transmitted radiation information. Method: Scatter patterns of several plastics, water, grocery-store tissues, and plastic phantoms were captured. Both high count (good statistics) and low count (realistic of clinical use) images were made using a Laue monochromator (33.17 keV) at the Canadian Light Source synchrotron. The primary beam was stopped with a tungsten bar. Arrays of up to 32×32 scatter patterns on a 1.25 mm pitch were recorded on a C9252DK-14 Hamamatsu flat panel sensor. A MATLAB routine was written to generate the profile of each scatter pattern and integrate over a given range to generate the pixel value. Results: Phantom images were generated with low count statistics. Different angular ranges provide different maximum tissue contrast. For example if fat-muscle contrast was important we would integrate from 2.25 to 4.00 degrees; similarly to maximize tissue-bone contrast we would integrate over the entire angle range (about 2.25 to 18.14 degrees, the angle limits determined by the acquisition geometry). The ideal angles are dependent on the beam energy. Images for a phantom composed of five plastics had significant contrast between each plastic. In a porcine sample there was significant contrast between muscle, fat and bone. Conclusions: Significant coherent scatter contrast between tissues has been demonstrated. Next, higher resolution images will be made. For comparison, conventional images will be generated simultaneously using a primary pencil beam detector. Additionally a multiplexed system comprising several pencil beams will be implemented to reduce the exposure time. Ultimately we will transfer the technology to standard hospital x-ray sources and detectors. Funding through Natural Sciences and Engineering Research Council of Canada (NSERC).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".