Online gamma-camera imaging of<sup>103</sup>Pd seeds (OGIPS) for permanent breast seed implantation
Bibliographic record
Abstract
Permanent brachytherapy seed implantation is being investigated as a mode of accelerated partial breast irradiation for early stage breast cancer patients. Currently, the seeds are poorly visualized during the procedure making it difficult to perform a real-time correction of the implantation if required. The objective was to determine if a customized gamma-camera can accurately localize the seeds during implantation. Monte Carlo simulations of a CZT based gamma-camera were used to assess whether images of suitable quality could be derived by detecting the 21 keV photons emitted from 74 MBq (103)Pd brachytherapy seeds. A hexagonal parallel hole collimator with a hole length of 38 mm, hole diameter of 1.2 mm and 0.2 mm septa, was modeled. The design of the gamma-camera was evaluated on a realistic model of the breast and three layers of the seed distribution (55 seeds) based on a pre-implantation CT treatment plan. The Monte Carlo simulations showed that the gamma-camera was able to localize the seeds with a maximum error of 2.0 mm, using only two views and 20 s of imaging. A gamma-camera can potentially be used as an intra-procedural image guidance system for quality assurance for permanent breast seed implantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".