Sci‐AM1 Sat ‐ 04: Gamma‐camera verification of breast brachytherapy seed distributions
Bibliographic record
Abstract
Objective: To assess whether a proposed SPECT device could resolve implanted breast brachytherapy seeds within an acceptable imaging time 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 seeds was feasible. A 5×5 cm detector, fitted with a hexagonal parallel hole collimator (hole length 38 mm, diameter 1.2 mm, septa 0.2 mm) was modeled. Projections were taken every 7.5 degrees, with a radius of rotation of 10 cm, and images reconstructed using an OSEM algorithm. The phantom modeled consisted of an 8 cm diameter sphere of breast tissue containing a central, 1 cm cubic distribution of 8 seeds, which were each 5 mm long and 0.8 mm wide titanium tubes with an inner radius of 0.32 mm. Results: An acquisition duration of 24 seconds yielded images with a FWHM of 6.0 mm and a scatter fraction of 8.2%. The error between the center of mass of the reconstructed image and the physical seed location was 0.81 ± 0.16 mm, when the phantom was imaged for 24 seconds. Conclusion: The online gamma‐camera approach to imaging the seeds is feasible for a simple seed distribution. The high contrast between the seeds and the non‐radioactive background allow a practical acquisition time of a minute or less. Additional simulations will be required to assess the system design for more realistic seed distributions in a geometry more closely modeling a patient.
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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 | 0.002 |
| 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) | 0.006 | 0.001 |
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".