Cone-beam CT dosimetry for the positional variation in isocenter: A Monte Carlo study
Bibliographic record
Abstract
This study investigated variations in kilovoltage cone-beam computed tomography (kV-CBCT) dosimetry for a change in the isocenter positions in the head-and-neck, lung-breast, and pelvis Rando phantoms using Monte Carlo simulations. Phase space kV-CBCT beams with energy of 120 kVp for the XVI system equipped to the Elekta Synergy medical linear accelerator were used to calculate dose distributions for the head-and-neck, lung-breast, and pelvis phantoms, with isocenters in the original positions near to the center of each phantom using the EGSnrc-based Monte Carlo code. Dose calculations were repeated with kV-CBCT isocenters shifted from the original positions along the x, y, and z axes. The relative dose ratio profiles, corresponding to different isocenter positions normalized to the original ones, were compared to evaluate the variations in the dose distribution due to isocentric shifts. Moreover, the relative dose at the isocenter, maximum dose, and mean dose for each isocenteric shift in the three phantoms were calculated. In this study, the original isocenter/center of the phantom was used as the reference for dose normalization. When the isocenter was chosen off from the center of the head-and-neck phantom, the maximum and mean imaging doses were found, varying by 14% and 15% in the anterior-posterior directions and 9% and 7% in the left-right directions. These dosimetric variations were slightly larger (17% and 17% in the anterior-posterior directions and 19% and 16% in the left-right directions) for the lung-breast phantom. For the pelvis phantom, the maximum and mean imaging doses varied by 10% and 22% in the anterior-posterior directions and 9% and 14% in the left-right directions with changes in the isocenter positions. Since the typical imaging dose is in the range of 2-8 cGy for the soft tissue per kV-CBCT acquisition, the variation in the imaging dose due to the patient setup error (approximately 2 cm) is insignificant compared to the original designed imaging dose. Moreover, the information on imaging dose varying as a function of the choices of isocenter or imaging center is useful for the physician and planner when they design the treatment plan for image-guided radiotherapy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".