SU‐F‐BRF‐14: Increasing the Accuracy of Dose Calculation On Cone‐Beam Imaging Using Deformable Image Registration in the Case of Prostate Translation
Bibliographic record
Abstract
Purpose: Artifacts can reduce the quality of dose re‐calculations on CBCT scans during a treatment. The aim of this project is to correct the CBCT images in order to allow for more accurate and exact dose calculations in the case of a translation of the tumor in prostate cancer. Methods: Our approach is to develop strategies based on deformable image registration algorithms using the elastix software (Klein et al., 2010) to register the treatment planning CT on a daily CBCT scan taken during treatment. Sets of images are provided by a 3D deformable phantom and comprise two CT and two CBCT scans: one of both with the reference anatomy and the others with known deformations (i.e. translations of the prostate). The reference CT is registered onto the deformed CBCT and the deformed CT serves as the control for dose calculation accuracy. The planned treatment used for the evaluation of dose calculation is a 2‐Gy fraction prescribed at the location of the reference prostate and assigned to 7 rectangular fields. Results: For a realistic 0.5‐cm translation of the prostate, the relative dose discrepancy between the CBCT and the CT control scan at the prostate's centroid is 8.9 ± 0.8 % while dose discrepancy between the registered CT and the control scan lessens to −2.4 ± 0.8 %. For a 2‐cm translation, clinical indices like the V90 and the D100 are more accurate by 0.7 ± 0.3 % and 8.0 ± 0.5 cGy respectively when using registered CT than when using CBCT for dose calculation. Conclusion: The results show that this strategy gives doses in agreement within a few percents with those from calculations on actual CT scans. In the future, various deformations of the phantom anatomy will allow a thorough characterization of the registration strategies needed for more complex anatomies.
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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) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".