X-ray forward-scatter imaging: Experimental validation of model
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Bibliographic record
Abstract
In our research program we have investigated, through modeling and related numerical calculations, the potential use of scattered photons for medical x-ray imaging. In this work, we present an experimental validation of the primary and of the forward-scatter x-ray imaging models. Incident polyenergetic photon beams generated from a conventional rotating anode x-ray tube were used. To compare quantitatively the results between primary and forward-scatter imaging, an ionization chamber was used to record the incident air collision kerma, Kair(c). Plots of contrast (C) and the signal-to-noise ratio (SNR) as a function of the imaging task are presented. We have chosen to make measurements with plastics [polymethyl methacrylate (PMMA), polycarbonate, polystyrene, polyethylene, and nylon] placed at the center of a 15 cm diam spherical water phantom. Good agreement between experiment (expt) and prediction (pred) was obtained for many imaging tasks. For example, to image a 2 cm thick PMMA/polycarbonate combination using an 80 kV beam with the primary photons we obtain Cexpt = 0.01 +/- 0.02, Cpred = 0.008 +/- 0.002, SNRexp/square root Kair(c) = 0.86 +/- 1.6(mJ/kg)(-1/2) and SNRpred/square root K(air)c = 0.51 +/- 0.14(mJ/kg)(-1/2). The values obtained by using the theta = 4 degrees scattered field were Cexpt = 0.26 +/- 0.06, Cpred = 0.19 +/- 0.01, SNRexp/square root Kair(c) = 3.8 +/- 0.8(mJ/ kg)(-1/2), and SNRpred/square root K(air(c) = 3.2 +/- 0.3 (mJ/kg)(-1/2) We have, however, shown that using form factor data from different authors can have a significant effect on the predicted values of C and SNR. The use of our semianalytic expressions for the numbers of transmitted and scattered photons combined with our experimental measurements allowed us to quantify the amount of water contamination in our measurements. Some preliminary results in air with biological materials (liver, muscle, water) are also presented. We are confident that our model can be used as a tool for designing and optimizing an x-ray scatter imaging system.
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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) | 0.002 | 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 it