Patient-specific dosimetry of conventional and intensity modulated radiation therapy using a novel full Monte Carlo phase space reconstruction method from electronic portal images
Bibliographic record
Abstract
Electronic portal imagers have promising dosimetric applications in external beam radiation therapy. In this study a patient dose computation algorithm based on Monte Carlo (MC) simulations and on portal images is developed and validated. The patient exit fluence from primary photons is obtained from the portal image after correction for scattered radiation. The scattered radiation at the portal imager and the spectral energy distribution of the primary photons are estimated from MC simulations at the treatment planning stage. The patient exit fluence and the spectral energy distribution of the primary photons are then used to ray-trace the photons from the portal image towards the source through the CT geometry of the patient. Photon weights which reflect the probability of a photon being transmitted are computed during this step. A dedicated MC code is used to transport back these photons from the source through the patient CT geometry to obtain patient dose. Only Compton interactions are considered. This code also produces a reconstructed portal image which is used as a verification tool to ensure that the dose reconstruction is reliable. The dose reconstruction algorithm is compared against MC dose calculation (MCDC) predictions and against measurements in phantom. The reconstructed absolute absorbed doses and the MCDC predictions in homogeneous and heterogeneous phantoms agree within 3% for simple open fields. Comparison with film-measured relative dose distributions for IMRT fields yields agreement within 3 mm, 5%. This novel dose reconstruction algorithm allows for daily patient-specific dosimetry and verification of patient movement.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".