SU‐E‐J‐233: Effect of Brachytherapy Seed Artifacts in T2 and Proton Density Maps in MR Images
Bibliographic record
Abstract
Purpose: This study aims at investigating the influence of brachytherapy seeds on T2 and proton density (PD) maps generated from MR images. Proton density maps can be used to extract water content. Since dose absorbed in tissue surrounding low energy brachytherapy seeds are highly influenced by tissue composition, knowing the water content is a first step towards implementing a heterogeneity correction algorithm using MR images. Methods: An LDR brachytherapy (IsoAid Advantage Pd‐103) seed was placed in the middle of an agar‐based gel phantom and imaged using a 3T Philips MR scanner with a 168‐channel head coil. A multiple echo sequence with TE=20, 40, 60, 80, 100 (ms) with large repetition time (TR=6259ms) was used to extract T2 and PD maps. Results: Seed artifacts were considerably reduced on T2 maps compared to PD maps. The variation of PD around the mean was obtained as −97% to 125% (±1%) while for T2 it was recorded as −71% to 24% (±1%). Conclusion: PD maps which are required for heterogeneity corrections are susceptible to artifacts from seeds. Seed artifacts on T2 maps, however, are significantly reduced due to not being sensitive to B0 field variation.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".