Sci‐PM Fri ‐ 08: Accurate surface dose measurements in CT exam using isotropic high sensitivity MOSFET dosimeters calibrated by Monte Carlo simulations
Bibliographic record
Abstract
Purpose: To measure surface dose in CT exam using isotropic high sensitivity MOSFET dosimeters calibrated by Monte Carlo (MC) simulations. Method and Materials: The EGSnrc MC code was used to model the x‐ray source of a Phillips PQ5000 CT simulator for energies ranging from 80 – 140 kVp. Half‐value layers and profiles measured in air were used as a means of validation for the CT x‐ray source model. A model of the MOSFET was build which allowed us to simulate their energy response. The MOSFET dosimeters (Thomson and Nielsen) were calibrated on the surface of a CTDI head phantom by comparing the measured dose (mV) to the calculated dose (mGy) in a voxel at the surface. Surface dose measurements were carried out on the CTDI phantom and compared with dose calculations. Results: The agreement between the simulated and measured HVL is within 2.2% over the whole kVp and filtration range. The measured and simulated profiles are within 3% up to 14 cm off‐axis and within 7.2 % over the full fan beam width considered in this study. Simulated and measured MOSFET energy response in‐air are within 5.4 % over 80–140 kVp range. The MOSFET exhibit an over‐response of about 10 % at 80 kVp compared to 140 kVp. Discrepancies between measured and calculated surface dose on the CTDI phantom increase as we move away from the point of dose normalization. Conclusion: MOSFET dosimeters offer a fast and easy means of accurately measuring surface dose in CT examinations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".