SU‐E‐I‐146: An X‐Ray Scatter Technique for Extracting Differential Linear Scattering Coefficients
Bibliographic record
Abstract
Purpose: To devise a scatter technique for extracting differential linear scattering coefficients of breast biopsies.Methods: An energy dispersive x‐ ray system is used to measure the scattered number of photons Ns(E) as a function of energy (E) from a 5 mm diameter 3 mm thick polycarbonate (lexan) biopsy at 6 degree, 12 degree, and 18 degree. A 50 kV 2.3 mA polychromatic pencil beam irradiates an area of 2.62 mm diameter on the sample for 3 minutes (X = 0.18 C/kg). A 25 mm̂2 by 1 mm thick CdTe detector is positioned 43 cm from the target with a 4.2 mm diameter aperture defining its active volume. Ns(E) spectra coupled with a semianalytic model are used to determine the differential linear scatter coefficients MUs(x) of biopsies, where x=E/(hc) sin(theta/2) is the momentum transfer argument. The Ns(E) spectrum for a biopsy of water was measured and was used with the model in reverse fashion to estimate the incident number of photons No. Water is chosen because diffraction data for water is considered the gold standard.Results: The MUs values were calculated using a bin size x = 0.06 nm̂−1 and E ranging from 7 to 40 keV. The values of MUs for lexan obtained at 6 degree using an No estimated from a 6 degree water scatter measurement were in good agreement with literature provided that a background subtraction correction is applied. At x=0.96 nm̂−1 the peak height is MUs=30.7 m̂−1 sr̂−1, a 2% overshoot. The MUs for lexan obtained from the 12 degree scatter measurement but with No estimated using the 18 degree scatter water measurement also resulted in good agreement with a 9% overshoot at the peak Conclusions: This work demonstrates a scatter technique with great potential for measuring the scatter signals of breast biopsies.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".