Development and assessment of a multi-beam continuous-phantom-motion x-ray scatter projection imaging system
Bibliographic record
Abstract
X-ray image formation using scattered radiation can yield a superior contrast-to-noise ratio compared to conventional transmission x-ray imaging. A barrier to practical implementation of scatter imaging systems has been slow image acquisition. We have developed a projection imaging system which uses five monoenergetic pencil beams in combination with continuous phantom motion to achieve acquisition times that are practical for medical and security applications. The system was configured at the Canadian Light Source synchrotron and consists of a primary collimator, motorized stages for phantom translation, a flat-panel x-ray detector for measuring scattered x rays, and photodiodes for simultaneously measuring transmitted x rays. Image generation requires several corrections to raw data artifacts arising from the nature of the detector, x-ray source, and acquisition procedure. We developed a novel correction for pixel location inaccuracy arising from continuous phantom motion. A five-beam system had nearly five times faster acquisition than a single-beam system. Continuous motion acquisition was approximately 30 times faster than step-and-shoot acquisition. The total acquisition time for a 9 cm × 5 cm phantom with 8425 pixels was just over 2 min. Image quality was also assessed, in part to determine its relation to acquisition speed. The width of sharp material boundaries was found to be at a minimum equal to the pencil beam width (1.75 mm) and to have an additional width equal to the product of the phantom translation speed and the acquisition time per pixel (up to 1.0 mm in our experiments). Contrast-detail performance was independent of acquisition speed, depending only on phantom entrance x-ray fluence. Pixel signal-to-noise ratio measurements indicate that detector readout noise is important for the scatter data, even for phantom air kerma as high as 30 mGy. Images could be improved with a detector having lower readout noise and higher sensitivity. Its spatial resolution could be moderate. We confirmed that for the same range of λ−1 sin(θ/2), where λ is the x-ray wavelength and θ is the scattering angle, scatter images acquired using different beam energies (33–70 keV) had nearly identical contrast.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".