Multi-center comparison of a PET/CT calibration phantom for imaging trials
Bibliographic record
Abstract
250 Objectives: We present the results of imaging a long-lived PET calibration phantom at multiple centers to assess how quantitative comparisons between the different scanners are affected by lesion size, scanner type, and local procedures. The goal is to determine the accuracy and precision due to instrumental factors for patient tracer uptake measurements in multi-center trials. Methods: The phantom, based on the NEMA NU-2 IQ phantom with Ge-68 (half life = 270 d) in epoxy, is designed to assess global accuracy, partial volume loss, reproducibility, and variations between scanners, protocols, and data reporting. The target:background ratio (T/B) for the 6 spheres was set to 4:1. Mean and max absolute activity concentration, T/B ratios, and SUV vs sphere diameter were measured. The phantom was imaged at 8 PET centers on 10 scanners manufactured by Siemens, Philips, and General Electric. Results: Average background SUV was 1.04±0.1. The recovery coefficient (RC) versus diameter depended on scanner type, image smoothing, whether absolute or relative mean or max values were reported. For typical clinical imaging protocols the coefficient of variation (COV) in RC across all scanners (averaged for all sphere diameters) was 8% if mean ROI values were used and 11% if max ROI values were used. Stochastic effects lead to a COV of approximately 3%, the residual COV due to deterministic differences between scanners and processing methods. Conclusions: The calibration phantom allows for direct comparison of quantitative results from sites in multi-center imaging trials using different scanners and/or different processing methods.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".