Quantitative comparison of 3D and 2D PET with brain studies
Bibliographic record
Abstract
The statistical superiority of 3D PET compared to 2D PET for brain neuroreceptor and 18-F-fluorodeoxyglucose (FDG) scanning is accepted. However, further validation of the quantitative aspects of data acquired in 3D mode is necessary. A 3D data processing protocol that involves iterative convolution subtraction scatter correction, detector normalization including axial and radial geometric factors, attenuation correction extracted from a 2D transmission scan, the Kinahan-Rogers reconstruction algorithm and region-of-interest based calibration factors was tested on 3D data from eleven human subjects using /sup 11/Cd-hydrotetrabenazine, /sup 11/C-Schering 23390 and FDG as tracers, 2D scans were performed on the same subjects and the DV, DVR and LMRGlu values obtained from these were used as reference. The comparison showed good quantitative agreement between the two acquisition modes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".