Time-of-flight scatter rejection in x-ray radiography
Bibliographic record
Abstract
Abstract Objective. Time-of-flight (TOF) scatter rejection allows for identifying and discarding scattered photons without the use of an anti-scatter grid (ASG). Although TOF scatter rejection was initially presented for cone-beam computed tomography, we propose, herein, to extend this approach to x-ray radiography. This work aims to evaluate with simulations if TOF scatter rejection can outperform ASGs for radiography. Approach. GATE was used to simulate the radiography of a head and a torso and a water cylinder with bone inserts in a system with total timing jitters from 0 ps up to 500 ps full-width-at-half-maximum. The transmission factor of TOF scatter rejection for primary and scattered photons was evaluated as if it were a virtual ASG. Main results. With a total timing jitter of 50 ps, TOF scatter rejection can reach a selectivity of 4.93 with a primary photons transmission of 99%. Reducing the timing jitter close to 0 ps increases the selectivity up to 15.85 for a head and torso radiography, outperforming typical ASGs which usually have a selectivity from 2.5 to 10 with a primary photons transmission from 50% to 70%. Significance. This suggests that TOF scatter rejection may be suitable to replace ASGs in applications requiring lower radiation exposure if sufficiently low timing jitter is achieved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".