Cone beam computerized tomography: the effect of calibration of the Hounsfield unit number to electron density on dose calculation accuracy for adaptive radiation therapy
Bibliographic record
Abstract
The availability of cone beam computerized tomography (CBCT) images at the time of treatment has opened possibilities for dose calculations representing the delivered dose for adaptive radiation therapy. A significant component in the accuracy of dose calculation is the calibration of the Hounsfield unit (HU) number to electron density (ED). The aim of this work is to assess the impact of HU to ED calibration phantom insert composition and phantom volume on dose calculation accuracy for CBCT. CBCT HU to ED calibration curves for different commercial phantoms were measured and compared. The effect of the scattering volume of the phantom on the HU to ED calibration was examined as a function of phantom length and radial diameter. The resulting calibration curves were used at the treatment planning system to calculate doses for geometrically simple phantoms and a pelvic anatomical phantom to compare against measured doses. Three-dimensional dose distributions for the pelvis phantom were calculated using the HU to ED curves and compared using Chi comparisons. The HU to ED calibration curves for the commercial phantoms diverge at densities greater than that of water, depending on the elemental composition of the phantom insert. The effect of adding scatter material longitudinally, increasing the phantom length from 5 cm to 26 cm, was found to be up to 260 HU numbers for the high-density insert. The change in the HU value, by increasing the diameter of the phantom from 18 to 40 cm, was found to be up to 1200 HU for the high-density insert. The effect of phantom diameter on the HU to ED curve can lead to dose differences for 6 MV and 18 MV x-rays under bone inhomogeneities of up to 20% in extreme cases. These results show significant dosimetric differences when using a calibration phantom with materials which are not tissue equivalent. More importantly, the amount of scattering material used with the HU to ED calibration phantom has a significant effect on the dosimetric accuracy, particularly in the radial direction.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".