Po‐Poster ‐ 24: A new tool to perform the timing alignment of the detectors in a PET scanner
Bibliographic record
Abstract
Timing alignment of the detectors in a PET Scanner is important in order to reduce the random counts with a major source of image noise in 3D whole body scans used in the evaluation of cancer patients. A new technique is described here and being tested on individual PET detectors at the Montreal Neurological Institute. A timing alignment probe manufactured by Scanwell Systems (Montreal, QC) uses a small quantity of Na‐22 embedded in plastic scintillator, which can detect each positron decay. The difference between the time of decay and time of gamma ray detection can be used to establish a time offset for each crystal in a PET Scanner. This paper reports the results from isolated PET detectors, since no PET Scanners presently have an input for the timing probe. We found that in fast detectors the timing alignment could be made with an accuracy of 120 ps in a ten minute scan for each crystal. Variations of up to 1.9 ns were observed from individual crystals from typical block detectors in conventional PET Scanners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.021 | 0.008 |
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".