Comparison of preclinical PET scanners: Pinhole collimation vs. electronic collimation
Bibliographic record
Abstract
493 Objectives The utility of PET in preclinical research is limited by spatial resolution and signal-to-noise ratio of the images. A recently developed PET system uses a clustered-pinhole collimator, enabling high-resolution, simultaneous imaging of PET and SPECT tracers. We investigated the potential of this design by direct comparison with a traditional PET scanner. Methods Two small animal PET scanners, one with electronic collimation (Siemens Focus120) and one with physical collimation using clustered pinholes (MILabs VECTor), were used to acquire data from Jaszczak and uniform phantoms. Mouse brain imaging using [18F]FDG PET was performed alongside quantitative ex-vivo autoradiography for reference. Bone imaging using [18F]NaF allowed comparison of imaging in the mouse body. Images were visually and quantitatively compared using measures of contrast and noise. Results Pinhole PET resolved the smallest rods (0.85 mm diameter) in the Jaszczak phantom, while coincidence PET resolved 1.1 mm diameter rods. Contrast-to-noise ratios were better for pinhole PET when imaging small rods ( Conclusions When small regions need to be resolved in scans with reasonably high activity or reasonably long scan times, a first generation clustered-pinhole system can provide superior image quality in terms of resolution, contrast and the contrast-to-noise ratio as compared to a traditional PET system. Research Support Canadian Institutes of Health Research, Pieken in de Delta grant PID06015, the Canadian Foundation for Innovation, the BC Knowledge Development Fund and the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.004 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".