Monte Carlo simulation of a computed tomography x-ray tube
Bibliographic record
Abstract
The dose delivered to patients during computed tomography (CT) exams has increased in the past decade. With the increasing complexity of CT examinations, measurement of the dose becomes more difficult and more important. In some cases, the standard methods, such as measurement of the computed tomography dose index (CTDI), are currently under question. One approach to determine the dose from CT exams is to use Monte Carlo (MC) methods. Since the patient geometry can be included in the model, Monte Carlo simulations are potentially the most accurate method of determining the dose delivered to patients. In this work, we developed a MC model of a CT x-ray tube. The model was validated with half-value layer (HVL) measurements and spectral measurements with a high resolution Schottky CdTe spectrometer. First and second HVL for beams without additional filtration calculated from the MC modelled spectra and determined from attenuation measurements differ by less than 2.5%. The differences between the first and second HVL for both filtered and non-filtered beams calculated from the MC modelled spectra and spectral measurements with the CdTe detector were less than 1.8%. The MC modelled spectra match the directly measured spectra. This works presents a first step towards an accurate MC model of a CT scanner.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".