Measurements on the timing stability of the MicroPET R4 animal PET scanner
Bibliographic record
Abstract
The MicroPET R4 and P4 small animal PET scanners have detectors consisting of an array of 64 2.1times2.1times10 LSO crystals coupled via light guides to Hamamatsu R5900-C12 position sensitive PMTs. These have energy resolutions in the range of 25% and timing resolutions <3 nsec. The calibration software allows users to set up the crystal identification maps and voltage to keV conversion gain for each crystal in a mostly automated and highly interactive way. However until recently there was no provision for making adjustments to the inter-detector timing alignment. Our preliminary evaluation of this instrument showed that reasonable normalization sinograms could only be obtained with a timing window of 10 nsec or more in spite of a timing resolution of <3 nsec. We performed sham transmission scans with nothing in the field of view, and a range of timing windows from 2 to 14 nsec and used a 14 nsec timing blank scan to generate effective attenuation sinograms as a function of timing window. These showed trues count-rates which fit well to a ERF(tau) function. However, the effective attenuation value, which should be 1.0, changes from block to block becomes very high (<3.5 at 6 nsec.) in some blocks suggesting the need for timing alignment. In the latest software release, V5.2.2.8, timing alignment is permitted, and the timing is much better aligned and much more stable
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".