SU‐E‐T‐399: CT Image Artifacts from Brachytherapy Seed Implants: The Impact of Seed Size and Motion
Bibliographic record
Abstract
Purpose: To determine the impact of seed thickness on post‐operative CT image quality, seed detection and dosimetry, with and without breathing motion. Methods: An acrylic prostate phantom mounted to a computer controlled motion platform was created for both standard (5 × 1 mm)‐ and small (5 × 0.5 mm)‐diameter non‐radioactive I‐125 seeds (Oncura Inc., Arlington Heights, IL). Seed arrangement within the phantom was based on a patient brachytherapy plan. Ten low‐contrast inserts, 2, 5, and 10 mm in diameter with contrast differences of 2, 12 and 29 HU, respectively, were embedded in the phantom. Volumetric CT scans were performed while the phantom exhibited vertical, longitudinal and elliptical motion cycles of amplitudes 0, 2, and 5mm. Streak artifacts were quantified by sampling the standard deviation (SD) of pixel values over regions of interest (ROI) within the phantom volume. Soft tissue contrast was quantified by calculating the contrast to noise ratio (CNR) for each low‐contrast insert. Seed localization and dosimetry were assessed using VariSeed v8.0 (Varian Medical Systems, Palo Alto, CA) with prostate, rectal and urethral volumes copied from the patient plan onto the phantom. Results: Image noise was reduced with the small seeds for no motion cases (SD 49 vs. 32HU) and for 5mm amplitude cases (SD 60 vs. 37HU). CNR with the small seeds was higher by a median value of 55% for the low‐contrast ROIs. Although VariSeed identified more erroneous seed positions for the small seeds, there were no clinically significant dosimetric differences to the prostate, rectal or urethral volumes. Conclusions: The use of smaller seeds for prostate brachytherapy results in improved image quality of post‐operative CT scans, without hindering the ability in localizing the seeds or affecting the dose to the prostate, rectum or urethra, irrespective of motion parameters. Seeds used in this study were donated by GE Healthcare.
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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 | 0.000 | 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".