Additional file 1 of Cross-validation study between the HRRT and the PET component of the SIGNA PET/MRI system with focus on neuroimaging
Bibliographic record
Abstract
Additional file 1: Supplementary Figure 1: Representative images of the image quality phantom data for different reconstruction parameters. Color scales of the images were normalized based on the average background value multiplied by the respective sphere to background ratio. Supplementary Figure 2: RCs (a) and %BG variability (b) as a function of sphere diameter for the image quality phantom. PET/MR data were reconstructed with TOF, TOF with filter, TOF with PSF, and TOF with PSF and filter. 2 iterations and 28 subsets were used, respectively (GE recommendation for phantom data). Note the gap between spheres marks cold vs. hot spheres. Supplementary Figure 3: RCs (%) of the PET/MR (a) as a function of sphere diameter for the contrast phantom with (solid lines) and without TOF (dotted lines). Comparison of RCs (b) without TOF of the PET/MR (solid lines) to the HRRT (dotted lines). Note the gap between spheres marks cold vs. hot spheres. Supplementary Figure 4: RCs (%) (PET/MR (a), HRRT (b)) versus sphere diameter for the contrast phantom. Data were analyzed with a single slice ROI (solid lines), the standard NEMA analysis method, and with a spherical VOI matching the physical sphere diameter (dotted lines). Note the gap between spheres marks cold vs. hot spheres. Supplementary Figure 5: Voxel-wise correlation of the HRRT to PET/MR activity concentration for each subject ([11C]DTBZ, [18F]FDG, [11C]raclopride). The black line indicates the identity line, the red dotted line displays the linear regression of the values with the corresponding R2. Supplementary Table 1: RCs (a) and %BG variability (b) for the image quality phantom scanned on the PET/MR. Supplementary Table 2: RCs for the contrast phantom scanned on the PET/MR and HRRT. PET/MR data were reconstructed with and without TOF information.
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.755 | 0.088 |
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".