Time-of-Flight Requirements to Reduce the Effect of Acollinearity on PET Spatial Resolution
Bibliographic record
Abstract
Spatial resolution in positron emission tomography (PET) is degraded by the acollinearity of annihilation photons (APA), resulting in a blurring of the order of 2 mm FWHM for whole-body PET scanners. We have previously shown that perfect Time-of-Flight (TOF) resolution can effectively mitigate this blurring and overcome the conventional (i.e., TOF-less) theoretical spatial resolution limit. However, the TOF resolution and coincidence event statistics required to achieve a noticeable improvement in spatial resolution remain unexplored. The purpose of this study is to investigate these requirements, specifically for whole-body and total-body scanners. Using a hypothetical $81-\mathrm{cm}$ diameter scanner with $2-\mathrm{mm}$ wide detectors, we show that ultrahigh TOF resolution (e.g., 13 ps FWHM) yields observable improvements in spatial resolution over a range of event statistics. Furthermore, this study reveals that lower TOF resolutions (e.g., 26 or 65 ps FWHM) may suffice to mitigate APA effects on spatial resolution, especially for the oblique tubes of response of large 3D systems, which are subject to more significant APA blurring. Therefore, ultrafast TOF would not only reduce APA blurring, but also alleviate the non-stationary nature of spatial resolution in long axial FOV total-body PET scanners.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".