NEMA NU 4-2008 Comparison of Preclinical PET Imaging Systems
Bibliographic record
Abstract
UNLABELLED: The National Electrical Manufacturers Association (NEMA) standard NU 4-2008 for performance measurements of small-animal tomographs was recently published. Before this standard, there were no standard testing procedures for preclinical PET systems, and manufacturers could not provide clear specifications similar to those available for clinical systems under NEMA NU 2-1994 and 2-2001. Consequently, performance evaluation papers used methods that were modified ad hoc from the clinical PET NEMA standard, thus making comparisons between systems difficult. METHODS: We acquired NEMA NU 4-2008 performance data for a collection of commercial animal PET systems manufactured since 2000: microPET P4, microPET R4, microPET Focus 120, microPET Focus 220, Inveon, ClearPET, Mosaic HP, Argus (formerly eXplore Vista), VrPET, LabPET 8, and LabPET 12. The data included spatial resolution, counting-rate performance, scatter fraction, sensitivity, and image quality and were acquired using settings for routine PET. RESULTS: The data showed a steady improvement in system performance for newer systems as compared with first-generation systems, with notable improvements in spatial resolution and sensitivity. CONCLUSION: Variation in system design makes direct comparisons between systems from different vendors difficult. When considering the results from NEMA testing, one must also consider the suitability of the PET system for the specific imaging task at hand.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".