Benefits of Modeling Annihilation Photon Acollinearity in the TOF System Matrix Model
Bibliographic record
Abstract
It was previously shown that, with sufficient TOF resolution and count statistics, a reduction in the blur induced by annihilation photons acollinearity (APA) in image space could be achieved. While this feature requires excellent TOF resolution, i.e., sub 70 ps, to start being noticeable, it does not require APA to be modeled in the system matrix. However, the introduction of such TOF resolution without modeling APA results in an inconsistency between the forward model and a dataset affected by APA. This inconsistency produces a highly oscillating profile when studying a scanner point spread function with a point source, which limits its usefulness to characterize the potential of TOF to mitigate the blur induced by APA. For completeness, we study here what happens when APA is modeled in the system matrix. We observe that the reconstruction of point source converge toward a stable point spread function, albeit with some Gibbs artifacts. We also show that having a TOF precision nearing the APA blur results in a gain in spatial resolution. Furthermore, it is shown that the spatial resolution achieved in a whole-body scanner and its long-axial Field-of-View (FOV) counterpart would be similar with 13 ps TOF resolution. This means that ultra-fast TOF resolution has the potential to enables more consistent spatial resolution within the FOV of whole-body and long axial FOV systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".