Timing resolution of a TOF-DOI detector module prototype for positron emission tomography
Bibliographic record
Abstract
Abstract To increase the image quality and spatial resolution in positron emission tomography (PET), organ-dedicated and large axial field of view scanners require precise correction of the parallax errors at oblique angles via depth-of-interaction methods. In addition to their large solid angle coverage, time-of-flight (TOF) information, i.e., a high coincidence time resolution (CTR), can be exploited to increase the image signal-to-noise ratio and effectively boost system sensitivity. Due to the impact of the photon travel time spread (PTS) on the TOF resolution, a high DOI resolution is essential to correct the impact of the PTS on the CTR and achieve even higher timing resolution. In this work, we present a TOF- and DOI-capable detector block with a CTR of 187 ps (FWHM) to 213 ps (FWHM), read out with the NINO and TOFPET2 ASIC, and a DOI resolution of 3.4 mm to 4.7 mm (RMSE), which distinguishes at least two DOI layers in the detector. Additionally, we established benchmarks for the CTR with custom high-frequency readout electronics. With these, a CTR of 136 ps (FWHM) can be maintained for a single TOF-DOI unit channel in the detector block.
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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 | 0.002 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".