Preliminary results of an embedded timing probe for calibrating PET scanner
Bibliographic record
Abstract
With the aim of improving the CNR of the LabPET II scanner, a method to correct the channel-to-channel coincidence time difference has been developed. The LabPET II, an avalanche photodiode-based PET scanner, features up to 55 000 channels which benefit from precise timing alignment. The correction process is fully automated and embedded in the scanner hardware and firmware. It uses a timing probe designed to react with the excess kinetic energy of a positron, enabling an absolute time reference of the positrons emission inside the probe. The time measurement is performed by a 312.5 ps time-to-digital converter (TDC) implemented in the LabPET II coincidence unit FPGA featuring a differential nonlinearity of -0.23 LSB peak and an integral nonlinearity of 0.14 LSB peak. The system computes probe-to-channel coincidences in order to obtain the absolute time difference between each channel of the scanner. The time correction is then applied to each channel to align all coincidence spectra, enhancing time resolution, thus improving CNR. Finally, these corrections are applied in real-time to each event during a typical PET acquisition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".