MO‐D‐134‐10: Using HVL and KVp to Portray An X‐Ray Source for Dose Calculations in CT
Bibliographic record
Abstract
Purpose: To show that the nominal peak tube voltage potential (kVp) and measured half‐value layer (HVL) are sufficient to generate energy spectra and fluence profiles for fast and accurate machine‐specific dose computations in Computed Tomography (CT). Methods: Spatial variation of the x‐ray source spectrum was found by measuring HVL across the internal bowtie filter axis and using the nominal kVp settings and third‐party software Spektr to generate the spectra. The beam fluence was calculated by multiplying in‐air dose measurements along the filter axis with the integral product of the spectra and the in‐air NIST mass‐energy attenuation coefficients. Dose calculations were performed using a previously validated in‐house hybrid deterministic and stochastic kV x‐ray dose computation algorithm (kVDoseCalc). To ensure dose convergence while minimizing calculation time, we examined the sensitivity of kVDoseCalc to the number of photons seeded. We modeled the source of a Philips Brilliance Big Bore CT scanner for 90, 120, and 140 kVp settings. Doses measured using a Farmer‐type Capintec ion chamber (0.6 cc) placed in a cylindrical poly methyl methacrylate (PMMA) phantom were compared to those computed with kVDoseCalc. Results: The number of photons seeded required to keep the average statistical uncertainty in dose less than .1% was found to be 1.25 million. The average percent difference between calculation and measurement pooled over all 12 positions in the phantom was found to be 1.68%, 1.60%, and 1.25% for 90, 120, and 140 kVp, respectively. The maximum percent difference between calculation and measurement was less than 3.64% pooled over all energies and measurement positions. Thirty‐one out of a total of 36 simulation conditions were within the experimental uncertainties associated with measurement reproducibility and chamber volume effects. Conclusion: Our source characterization technique, which derives incident fluence and spectra from measurements of HVL across the bowtie profile, is sufficient for accurate machine‐specific CT dose computations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".