Sci-Thurs PM: Planning-11: An Epid-Based Monte Carlo Approach to In-Vivo Dosimetry for Intensity-Modulated Radiation Therapy Treatments
Bibliographic record
Abstract
As intensity-modulated-radiation therapy (IMRT) becomes more prevalent, the importance of determining, and accounting for, treatment planning errors and patient setup errors, which may result in discrepancies between the calculated and actual delivered dose concurrently increases. Such errors include Multi-Leaf Collimator (MLC) mis-calibration, organ motion, the tongue-and-groove effect, etc. Precise Monte Carlo-based modeling of radiation transport through all components of IMRT-capable linacs overcomes some of these deficiencies, but the complexity of transport through the MLC is an impediment to clinically-timely implementation. Also, not all delivery errors can be accounted for with greater care in MLC simulation e.g. differences in MLC calibration. However, use of the amorphous-silicon detector (or EPID, for Electronic Portal Imaging Device), available on many linacs, provides a solution; appropriately deconvolving EPID-captured beam images provides the particle fluence at the EPID. Back-projected to MLC height, this array can be used to modulate an existing phase space file, scored at the same height, such that direct simulation of these particles through the MLC is not required. This method was compared against our centre's treatment planning system, based on Pencil Beam Convolution (PBC), for various MLC configurations. The new method is promising, in that it matched well with film measurements, resulting in approximately 90% of pixels having Gamma-Dose (3mm Distance-To-Agreement, 3% Dose-Difference criteria) less than 1, versus 72% to 84% for the PBC-based dose distributions. By utilizing Cone Beam CT to account for any setup errors or physiological changes, we believe this method will provide for more accurate 3D in-vivo dosimetry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".