Accurate patient dosimetry of kilovoltage cone‐beam CT in radiation therapy
Bibliographic record
Abstract
The increased utilization of x-ray imaging in image-guided radiotherapy has dramatically improved the radiation treatment and the lives of cancer patients. Daily imaging procedures, such as cone-beam computed tomography (CBCT), for patient setup may significantly increase the dose to the patient's normal tissues. This study investigates the dosimetry from a kilovoltage (kV) CBCT for real patient geometries. Monte Carlo simulations were used to study the kV beams from a Varian on-board imager integrated into the Trilogy accelerator. The Monte Carlo calculated results were benchmarked against measurements and good agreement was obtained. The authors developed a novel method to calibrate Monte Carlo simulated beams with measurements using an ionization chamber in which the air-kerma calibration factors are obtained from an Accredited Dosimetry Calibration Laboratory. The authors have introduced a new Monte Carlo calibration factor, fMCcal, which is determined from the calibration procedure. The accuracy of the new method was validated by experiment. When a Monte Carlo simulated beam has been calibrated, the simulated beam can be used to accurately predict absolute dose distributions in the irradiated media. Using this method the authors calculated dose distributions to patient anatomies from a typical CBCT acquisition for different treatment sites, such as head and neck, lung, and pelvis. Their results have shown that, from a typical head and neck CBCT, doses to soft tissues, such as eye, spinal cord, and brain can be up to 8, 6, and 5 cGy, respectively. The dose to the bone, due to the photoelectric effect, can be as much as 25 cGy, about three times the dose to the soft tissue. The study provides detailed information on the additional doses to the normal tissues of a patient from a typical kV CBCT acquisition. The methodology of the Monte Carlo beam calibration developed and introduced in this study allows the user to calculate both relative and absolute absorbed doses.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".