Optimum momentum transfer arguments for x‐ray forward scatter imaging
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Bibliographic record
Abstract
In our research program we have shown through modeling, related numerical calculations, and experimental measurements that there exists a potential use of scattered radiation for medical x-ray imaging. Each incident photon of wavelength lambda which scatters at a small angle theta with respect to its initial direction of travel has a change in momentum characterized by the photon momentum transfer argument x = lambda(-1) sin(theta/2). In this work, we show that in order to maximize the signal-to-noise ratio (SNR) obtained with scattered x rays, one must detect photons with specific x values. Using a photon counting detector to distinguish 2-cm-thick polymethyl methacrylate and nylon targets situated within a 15-cm-diam spherical water phantom with an 80 kV beam yields experimentally SNR/square root(K(air)c) = 12.8 +/- 0.2 (mJ/kg)(-1/2) when using the photons between x = 0.5 and 0.7 nm(-1). Here K(air)c is the air collision kerma and the average momentum transfer argument, x, is calculated by weighting x by the incident photon fluence distribution. The model predicts a value of SNR/square root(K(air)c) = 12.9 (mJ/kg)(-1/2). If we choose to form the signal with the range in x extended to be from 0.5 to 1.0 nm(-1) then, despite the detection of more scattered photons, experimentally SNR/square root(K(air)c) decreases by 38% to 7.9 +/- 0.3 (mJ/kg)(-1/2). The model predicts a value of 9.46 (mJ/kg)(-1/2). Results for energy integrating detectors are in general similar to those for photon counters, but there exist cases where a significant decrease in SNR can occur. For example, for measurements in air with the two plastics at theta = 3 degrees the SNR for an energy integrator was found to be 52% that of a photon counter. Numerical calculations predict that the effects of spectral blur can be significant when a narrow angular range is used for detection. Preliminary numerical predictions for breast tissues suggest a potential use of x-ray scatter in the field of mammography.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it