An investigation of entrance surface dose calculations for diagnostic radiology using Monte Carlo simulations and radiotherapy dosimetry formalisms
Bibliographic record
Abstract
Our aim in this work was to investigate the methodology used in the determination of the entrance surface dose (ESD) in diagnostic radiology. In kV x-rays for low-energy photons (tube potential up to 160 kV, HVL: 1-8 mm Al), the ESD is based on the use of the ratio of mass-energy absorption coefficients and backscatter factors. A full simulation of the photon and electron transport in a kilovoltage x-ray unit, using the Monte Carlo code BEAM/EGS4, was performed to obtain an accurate beam phase space for use in dose calculation. The modelled phase space was experimentally validated for the beam qualities (measured HVL: 3.3 mm Al-2.2 mm Cu) and showed good agreement between calculated and measured HVLs, air kerma and relative dose distributions. We have computed the conversion factors from air kerma to water or soft tissue absorbed dose at the surface of a phantom for beam qualities (HVL: 3.3-8.35 mm Al). The same model was also used to calculate the ESD in water and in soft tissue for the low-energy photon range considered. The results show that the numerical differences between the air kerma and the water kerma based backscatter factors are insignificant. The same conclusion was reached for the (mu(en)/rho) ratios, for soft tissue to air, evaluated using either the primary photon spectra or the spectra at the surface of a phantom. Furthermore, the good agreement obtained for the computation of the conversion factors with a full BEAM/EGS4 model confirms the previous studies which are based on different sources for the spectral distribution and different beam geometries (pencil beam or point source assumptions). On the other hand, the ESD in water or soft tissue is well described either with the B(air) or the B(w) formalism. Conversion factors from air kerma to ESD in these media are proposed in this work for several beam qualities in diagnostic radiology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".