Experimental validation of a coincidence time resolution metric including depth-of-interaction bias for TOF-PET
Bibliographic record
Abstract
Depth-of-interaction (DOI) variability of annihilation photons is known to be a source of coincidence time resolution (CTR) degradation for fast time-of-flight-positron emission tomography detectors. An analytical model was recently proposed to explicitly include the DOI time bias separately from variance-related statistical factors, such as scintillation photon emission and photosensor jitter, in the CTR evaluation. In the present work, an experimental validation of this new model is provided. An unconventional signal readout configuration was used to magnify the DOI bias with 20 mm long LYSO:Ce crystals. In a head-to-head orientation of the crystals, simulations performed using the metric with DOI bias exhibited a much better agreement (within 21 ps) with the experimentally measured CTR of 413 ± 8 ps full-width at half maximum, whereas simulations without DOI bias underestimated the CTR by 138 ps. The metric including DOI bias was shown to also be effective at predicting the CTR of the head-to-head setup (without DOI information) using data from a DOI-collimated experimental setup (with partial DOI information). With the development of new low-variance ultra-fast detectors, the DOI timing blur will become increasingly important and will need to be taken into account in analytical predictions and in some experimental measurements through the proposed metric.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".