<title>Compton scatter in frequency space: a theoretical study</title>
Bibliographic record
Abstract
For x-ray detectors, Compton interactions deposit photon energies along the paths of recoil electrons, which are not isotropic about the primary interaction sites. Light from each interacting x-ray is only generated near the path of a recoil electron. In this study, Compton scatter is modeled as an input-labeled cascade of the amplification and scattering processes to describe the transfer relationship of signal and noise in frequency space. The output of the model is the spatial distribution of secondary quanta generated by Compton recoil electrons. We determine the spatial dependence and statistical correlation of secondaries from the initial energy of the recoil electron and its range, resulting in the 'Compton' modulation transfer function (MTF) and noise power spectrum (NPS), respectively. Then the 'Compton' MTF and NPS are used to calculate the 'Compton' detective quantum efficiency (DQE). The probability density function of scattering angle of Compton recoil electron is developed using the Klein-Nishina coefficients. Results are applied to the description of a portal imaging system at 6 approximately MV where non-Compton interactions can be ignored. The MTF results are compared with a Monte Carlo calculation. This is the first model of how Compton interactions in the metal-plate/phosphor combination degrade image quality in terms of signal and noise. It is shown that Compton MTF depends on energy of x-ray photon in a complex way, and Compton scatter imposes a fundamental limitation on both the MTF and DQE of x-ray imaging system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".