Normalization in 3D PET: comparison of detector efficiencies obtained from uniform planar and cylindrical sources
Bibliographic record
Abstract
The authors have performed a comparison between 3D PET Normalization Factors (NFs) obtained from a uniform planar source and a uniform cylindrical phantom. The NFs have geometric and detector efficiency components. Detector efficiency data were measured using both phantoms. Both efficiency data sets were corrected using geometric factors obtained from a low-scattering planar distribution. NFs derived from 3D planar and 3D cylindrical efficiency data were applied to the sinogram data, yielding axial uniformity indices of 0.78% (2D), 1.71% (3D planar), and 3.40% (3D cylinder), and respective radial uniformity indices of 2.00%, 4.81%, and 4.07%. Correcting the cylinder for scatter prior to calculating the detector efficiencies was found to slightly improve axial uniformity and to slightly degrade radial uniformity. The uniformity obtained from cylinder-derived detector efficiencies is nearly identical to that obtained from plane-source derived efficiencies; the predominant influence on the accuracy of the normalization is the geometric factors used to correct the detector efficiency data.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".