SU‐E‐J‐05: A Dose‐Based Metric to Assess the Accuracy of Deformable Image Registration
Bibliographic record
Abstract
Purpose: To examine the correlation between landmark‐based spatial registration error and a novel metric for evaluating dose mapping error. Methods: The dose mapping error was calculated from the difference between the dose distributions obtained using two different dose mapping techniques. One method calculates the dose on a deformed geometry while the other calculates the dose on the target geometry. In both cases the dose distributions are mapped in a consistent manner back to the reference geometry. For a realistic clinical lung plan, dose distributions were mapped to the Exhale phase for repeated deformable registrations with different average spatial registration accuracies as determined by distance to agreement of 300 manually identified landmarks. The dose mapping error and spatial registration error were compared at each landmark and the correlation was assessed. Results: No clear correlation was observed between spatial registration accuracy and dose mapping error, however, the average dose mapping error was found to increase as average spatial registration accuracy increased. The relationship between the dose mapping error and spatial error at each landmark point was found to be influenced by the dose gradient, density gradients and voxel size. Conclusion: The dose mapping error was found to not have any direct relationship with spatial registration error but depends on a number of factors including: dose gradient, density gradient and voxel size. Our results indicate that landmark analysis on its own is not a sufficient predictor of the accuracy of dose mapping and that metrics that directly assess the dose mapping error should be used. Natural Sciences and Engineering Research Council
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".