Pitfall in the Implementation of Acollinearity in PET Monte Carlo Simulation Softwares
Bibliographic record
Abstract
Acollinearity of annihilation photons is a source of spatial blur in PET imaging that increases with the scanner radius. It was demonstrated that acollinearity follows a Gaussian distribution. This statement can refer to two concepts: the amplitude of the acollinearity angle or the angular deviation of the annihilation photons relative to the collinear case. Since the former is the partial integral of the latter, an error of interpretation could have significant repercussions. Previous works that have studied the effect of acollinearity in PET imaging have made the assumption that the angular deviation is Gaussian, which is in agreement with experimental studies. However, we show that in GATE, a PET simulation software, acollinearity is simulated as the former. This not only changes the shape of the spatial blur induced in the image space but also significantly reduces its effect on spatial resolution, e.g., from 2.1 mm FWHM to 0.4 mm FWHM for a PET scanner ≈ 80 cm in diameter. This underestimation is shared with other PET simulation softwares. Results obtained with these simulators would underestimate, sometimes severely, the blur induced by acollinearity. We propose an approach to simulate acollinearity that follows the latter interpretation and show that it would be adequate for PET imaging.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".