NECR analysis of 3D brain PET scanner designs
Bibliographic record
Abstract
A dedicated 3D brain PET scanner has several advantages, most notably increased sensitivity, over a whole body scanner for neurological studies. However brain scanners have higher scatter fractions, random count-rates and deadtime for the same activity concentration. We have used noise effective count-rate (NECR) analysis to compare brain scanners of 53, 60, and 66 cm diameter with the GE ADVANCE whole body scanner (93 cm diameter). Monte Carlo simulations of a brain-sized phantom (16 cm diameter, 13 cm length) in the ADVANCE geometry were used to develop a model for NECR performance, which was reconciled to results from a decay series measurement. The model was then used to predict the performance of the brain scanner designs. The brain scanners have noise effective sensitivities (the slope of the NECR curve at zero activity) as much as 40% higher than ADVANCE. However, their NECR advantage diminishes quickly as the activity concentration increases; the brain scanners' NECR equals ADVANCE at /spl sim/0.3 /spl mu/Ci/cc, and ADVANCE has superior NECR performance at higher activity levels. An imaging center concentrating on only very low activity imaging tasks would find the efficiency advantage of a smaller detector diameter valuable, while a center performing higher activity studies such as bolus water injections or 5 mCi FDG injections might prefer the count rate performance of a whole body scanner.>
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".