MO‐AB‐BRA‐03: Development of Novel Real Time in Vivo EPID Treatment Verification for Brachytherapy
Bibliographic record
Abstract
Purpose: High Dose Rate (HDR) brachytherapy treatments are employed worldwide to treat a wide variety of cancers. However, in vivo dose verification remains a challenge with no commercial dosimetry system available to verify the treatment dose delivered to the patient. We propose a novel dosimetry system that couples an independent Monte Carlo (MC) simulation platform and an amorphous silicon Electronic Portal Imaging Device (EPID) to provide real time treatment verification. Methods: MC calculations predict the EPID response to the photon fluence emitted by the HDR source by simulating the patient, the source dwell positions and times, and treatment complexities such as tissue compositions/densities and different applicators. Simulated results are then compared against EPID measurements acquired with ∼0.14s time resolution which allows dose measurements for each dwell position. The EPID has been calibrated using an Ir‐192 HDR source and experiments were performed using different phantoms, including tissue equivalent materials (PMMA, lung and bone). A source positioning accuracy of 0.2 mm, without including the afterloader uncertainty, was ensured using a robotic arm moving the source. Results: An EPID can acquire 3D Cartesian source positions and its response varies significantly due to differences in the material composition/density of the irradiated object, allowing detection of changes in patient geometry. The panel time resolution allows dose rate and dwell time measurements. Moreover, predicted EPID images obtained from clinical treatment plans provide anatomical information that can be related to the patient anatomy, mostly bone and air cavities, localizing the source inside of the patient using its anatomy as reference. Conclusion: Results obtained show the feasibility of the proposed dose verification system that is capable to verify all the brachytherapy treatment steps in real time providing data about treatment delivery quality and also applicator/structure motion during or between treatments.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".