SU‐FF‐I‐81: Gamma Camera Guided Permanent Breast 103Pd Seed Implantation
Bibliographic record
Abstract
Objective: To assess whether a proposed SPECT device could address the requirements of resolving distributions of permanent breast brachytherapy seeds following implantation; while maintaining an acceptable imaging time to allow for correction of misplaced seeds. Method: Monte Carlo simulations of a cadmium zinc telluride crystal‐based gamma camera were used to assess whether the detection of 22 keV photons emitted from the seeds was feasible. A hexagonal parallel hole collimator, hole length 38 mm, diameter 1.2 mm with 0.2 mm septa was modeled. The design of the gamma camera device was evaluated on two phantom models. The first model consisted of a simple representation of the clinical problem by simulating the breast as 8 cm diameter sphere of breast tissue containing a central, 1cm cubic distribution of 8 seeds. The second simulation presented a more accurate depiction of the clinical problem, where the breast model was based on the pre‐implant CT scan of a typical breast brachytherapy patient and the activity was simulated from the patient's corresponding treatment plan. Results: The spherical phantom yielded promising results after 24 s of imaging time, where the maximum error between the center of mass of the seeds in the reconstructed image and the simulated seed location was 1.02 mm. The results from the clinically accurate simulation revealed that individual seeds could not be identified from the reconstructed images after 2 minutes of imaging. However, the strands of seeds, arranged in each needle were localized to a maximum error of 1.9 mm. Conclusion: The online gamma‐camera approach to imaging the seeds is feasible for simple seed distributions. Additional improvements to the collimator design and the gamma camera orbit are required before the gamma camera device will be able to distinguish each seed in an implanted seed distribution.
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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 | 0.000 | 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) | 0.001 | 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".