Development of virtual patient models for permanent implant brachytherapy Monte Carlo dose calculations: interdependence of CT image artifact mitigation and tissue assignment
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Bibliographic record
Abstract
This work investigates and compares CT image metallic artifact reduction (MAR) methods and tissue assignment schemes (TAS) for the development of virtual patient models for permanent implant brachytherapy Monte Carlo (MC) dose calculations. Four MAR techniques are investigated to mitigate seed artifacts from post-implant CT images of a homogeneous phantom and eight prostate patients: a raw sinogram approach using the original CT scanner data and three methods (simple threshold replacement (STR), 3D median filter, and virtual sinogram) requiring only the reconstructed CT image. Virtual patient models are developed using six TAS ranging from the AAPM-ESTRO-ABG TG-186 basic approach of assigning uniform density tissues (resulting in a model not dependent on MAR) to more complex models assigning prostate, calcification, and mixtures of prostate and calcification using CT-derived densities. The EGSnrc user-code BrachyDose is employed to calculate dose distributions. All four MAR methods eliminate bright seed spot artifacts, and the image-based methods provide comparable mitigation of artifacts compared with the raw sinogram approach. However, each MAR technique has limitations: STR is unable to mitigate low CT number artifacts, the median filter blurs the image which challenges the preservation of tissue heterogeneities, and both sinogram approaches introduce new streaks. Large local dose differences are generally due to differences in voxel tissue-type rather than mass density. The largest differences in target dose metrics (D90, V100, V150), over 50% lower compared to the other models, are when uncorrected CT images are used with TAS that consider calcifications. Metrics found using models which include calcifications are generally a few percent lower than prostate-only models. Generally, metrics from any MAR method and any TAS which considers calcifications agree within 6%. Overall, the studied MAR methods and TAS show promise for further retrospective MC dose calculation studies for various permanent implant brachytherapy treatments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it