PET Detector Quality Assurance Using$^137$Cs Singles Transmission Imaging
Bibliographic record
Abstract
Daily quality assurance of positron emission tomography detectors, to ensure accurate efficiency calibration of the system, typically requires the acquisition of coincidence data from a flood source. To obtain sufficient counts, long scan times on the order of an hour are required. The long acquisition time and necessity of an external activity source may make such a protocol impractical for daily use. A new protocol has been demonstrated for the ECAT ART scanner, using the two <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$^137$</tex> Cs transmission sources. The new protocol produces a map of the counts per detector, as well as plots of the mean and standard deviation of the detector counts in a given block. Graphical outputs allow for quick recognition of problematic areas. The factory protocol requires 90 min to obtain a 1% statistical error on the individual detector counts. Due to the high activity of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$^137$</tex> Cs sources, a 1% statistical error is obtained with an 8 min scan using the new protocol. The new protocol provides information regarding the status of all blocks, reduces acquisition time, and alleviates the need for an external activity source. The protocol presented in this study can be implemented on other <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$^137$</tex> Cs transmission based scanners, and with coincidence flood sources if detector-specific singles histogramming is available.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".