TU-F-CAMPUS-I-02: Validation of a CT X-Ray Source Characterization Technique for Dose Computation Using An Anthropomorphic Thorax Phantom
Bibliographic record
Abstract
Purpose: To experimentally validate a rotational kV x-ray source characterization technique by computing CT dose in an anthropomorphic thorax phantom using an in-house dose computation algorithm (kVDoseCalc). Methods: The lateral variation in incident energy spectra of a GE Optima big bore CT scanner was found by measuring the HVL along the internal, full bow-tie filter axis. The HVL and kVp were used to generate the x-ray spectra using Spektr software, while beam fluence was derived by dividing the integral product of the spectra and in-air mass-energy absorption coefficients by in-air dose measurements along the bow-tie filter axis. Beams produced by the GE Optima scanner were modeled at 80 and 140 kVp tube settings. kVDoseCalc calculates dose by solving the linear Boltzmann transport equation using a combination of deterministic and stochastic methods. Relative doses in an anthropomorphic thorax phantom (E2E SBRT Phantom) irradiated by the GE Optima scanner were measured using a (0.015 cc) PTW Freiburg ionization chamber, and compared to computations from kVDoseCalc. Results: The agreement in relative dose between dose computation and measurement for points of interest (POIs) within the primary path of the beam was within experimental uncertainty for both energies, however points outside the primary beam were not. The average absolute percent difference for POIs within the primary path of the beam was 1.37% and 5.16% for 80 and 140 kVp, respectively. The minimum and maximum absolute percent difference for both energies and all POIs within the primary path of the beam was 0.151% and 6.41%, respectively. Conclusion: The CT x-ray source characterization technique based on HVL measurements and kVp can be used to accurately compute CT dose in an anthropomorphic thorax phantom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".