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 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 | 0.000 | 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".