Algorithm and simulation for real‐time positron emission based tumor tracking using a linear fiducial marker
Bibliographic record
Abstract
The effectiveness of radiotherapy in cancer treatment remains significantly limited by the accuracy of tumor dose delivery. The ideal solution lies in real-time localization of patient tumors during therapy; one such method is by tracking implanted low-activity positron emitters using two pairs of orthogonally placed gamma-ray detectors. Prior studies have examined multiple point sources, which have potential patient complications during implantation. A linear source geometry is proposed as a less invasive alternative, with potential higher-precision tracking. A source localization algorithm has been devised using cost-function minimization of the source position estimate relative to annihilation gamma coincidence lines. The algorithm was tested via Monte Carlo simulation methods using a Geant4 application for emission tomography (GATE) package for a source of length of 2.00 cm and width of 0.1 mm. The midpoint of the linear marker was located within submillimeter accuracy at 200 coincidence events and the orientation of the source determined with less than 5 degrees (0.087 rad) angular deviation at 300 events. At an optimal event count of 700, tracking had mean midpoint error of 0.48 +/- 0.26 mm and mean angular deviation of 0.041 +/- 0.023 rad (1.4 degrees +/- 0.8 degree). The source and tracking algorithm may prove effective for future clinical implementation in radiotherapy treatment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".