Experimental evaluation of an online gamma‐camera imaging of permanent seed implantation (OGIPSI) prototype for partial breast irradiation
Bibliographic record
Abstract
Previously, our team used Monte Carlo simulation to demonstrate that a gamma camera could potentially be used as an online image guidance device to visualize seeds during permanent breast seed implant procedures. This could allow for intraoperative correction if seeds have been misplaced. The objective of this study is to describe an experimental evaluation of an online gamma-camera imaging of permanent seed implantation (OGIPSI) prototype. The OGIPSI device is intended to be able to detect a seed misplacement of 5 mm or more within an imaging time of 2 min or less. The device was constructed by fitting a custom built brass collimator (16 mm height, 0.65 mm hole pitch, 0.15 mm septal thickness) on a 64 pixel linear array CZT detector (eValuator-2000, eV Products, Saxonburg, PA). Two-dimensional projection images of seed distributions were acquired by the use of a digitally controlled translation stage. Spatial resolution and noise characteristics of the detector were measured. The ability and time needed for the OGIPSI device to image the seeds and to detect cold spots was tested using an anthropomorphic breast phantom. Mimicking a real treatment plan, a total of 52 103Pd seeds of 65.8 MBq each were placed on three different layers at appropriate depths within the phantom. The seeds were reliably detected within 30 s with a median error in localization of 1 mm. In conclusion, an OGIPSI device can potentially be used for image guidance of permanent brachytherapy applications in the breast and, possibly, other sites.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".