SU‐E‐T‐501: A Sensitivity Study of Tissue Characterization for Brachytherapy Monte Carlo Dose Calculation
Bibliographic record
Abstract
Purpose: To establish the reliability of electron density (ED) mapping and tissue segmentation techniques using CT images for brachytherapy, considering stochastic and systematic Hounsfield unit (HU) variations. Methods: Most common artifacts are simulated within a Monte Carlo theoretical framework. A set of CT data is first generated with the EGSnrc suite followed by their reconstruction performed with an iterative algorithm. A statistical analysis of HU values from reconstructed images is performed to determine uncertainties and systematic effects introduced by the iterative algorithm and the presence of artifacts. Maps of ED and tissue indexes are retrieved from an HU‐ED curve calibrated at 120 kVp using experimental measurements and ICRU data. The mean energy absorption coefficient for each tissue is computed from ICRU data for an Iridium‐192 source in order to evaluate the impact on dose calculations. A probabilistic approach is used to compute the uncertainty on absorbed dose generating random distributions of HU and determining the probabilistic effects on the extracted ED and absorption coefficients. Results: For an uncertainty of +/− 20 HU, absorption coefficient uncertainties raise up to 3% when HU values are near the fat‐muscle intersection and up to 9% near the muscle‐spongiosa intersection. Uncertainties on ED are found to be less than 1% for HU above 0 and up to 3% for fat. A systematic effect of 50 HU caused by typical artifacts yields absorption coefficient errors up to 30% for HU values near the muscle‐spongiosa region, and errors in ED up to 8% for fat. Conclusion: Results show that the main source of dose calculation uncertainty is caused by the sensitivity of the tissue segmentation technique. This study suggests that improvements in such techniques are yet to be achieved.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".