Monte Carlo modelling of singles-mode transmission data for small animal PET scanners
Bibliographic record
Abstract
The attenuation corrections factors (ACFs), which are necessary for quantitatively accurate PET imaging, can be obtained using singles-mode transmission scanning. However, contamination from scatter is a largely unresolved problem for these data. We present an extension of the Monte Carlo simulation tool, GATE, for singles-mode transmission data and its validation using experimental data from the microPET R4 and Focus 120 scanners. We first validated our simulated PET scanner for coincidence-mode data where we found that experimental resolution and scatter fractions (SFs) agreed well for simulations that included positron interactions and scatter in the source material. After modifying GATE to model singles-mode data, we compared simulated and experimental ACFs and SFs for three different sized water cylinders using 57Co (122 keV photon emitter) and 68Ge (positron emitter) transmission sources. We also propose a simple correction for a large background contamination we identified in the 68Ge singles-mode data due to intrinsic 176Lu radioactivity present in the detector crystals. For simulation data, the SFs agreed to within 1.5% and 2.5% of experimental values for background-corrected 68Ge and 57Co transmission data, respectively. This new simulation tool accurately models the photon interactions and data acquisition for singles-mode transmission scans.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".