Backscatter factors and mass energy-absorption coefficient ratios for diagnostic radiology dosimetry
Bibliographic record
Abstract
Backscatter factors, B, and mass energy-absorption coefficient ratios, (μ(en)/ρ)(w, air), for the determination of the surface dose in diagnostic radiology were calculated using Monte Carlo simulations. The main purpose was to extend the range of available data to qualities used in modern x-ray techniques, particularly for interventional radiology. A comprehensive database for mono-energetic photons between 4 and 150 keV and different field sizes was created for a 15 cm thick water phantom. Backscattered spectra were calculated with the PENELOPE Monte Carlo system, scoring track-length fluence differential in energy with negligible statistical uncertainty; using the Monte Carlo computed spectra, B factors and (μ(en)/ρ)(w, air) were then calculated numerically for each energy. Weighted averaging procedures were subsequently used to convolve incident clinical spectra with mono-energetic data. The method was benchmarked against full Monte Carlo calculations of incident clinical spectra obtaining differences within 0.3-0.6%. The technique used enables the calculation of B and (μ(en)/ρ)(w, air) for any incident spectrum without further time-consuming Monte Carlo simulations. The adequacy of the extended dosimetry data to a broader range of clinical qualities than those currently available, while keeping consistency with existing data, was confirmed through detailed comparisons. Mono-energetic and spectra-averaged values were compared with published data, including those in ICRU Report 74 and IAEA TRS-457, finding average differences of 0.6%. Results are provided in comprehensive tables appropriated for clinical use. Additional qualities can easily be calculated using a designed GUI interface in conjunction with software to generate incident photon spectra.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".