A novel method for harmonization of PET image spatial resolution without phantoms
Bibliographic record
Abstract
Abstract The most common approach for estimating the spatial resolution of PET images in multi-center studies typically uses Hoffman phantom data as a surrogate. Specifically, the phantom-based matching resolution approach assumes that scanned phantom PET images are well approximated by a ground truth, noise-free digital phantom convolved with a Gaussian kernel of unknown size. The size of the kernel is then estimated by an exhaustive search on the amount of blurring needed to match the smoothed digital phantom to a particular scanned phantom image. Unfortunately, Hoffman phantom images may not always be readily available, and phantom-based approaches may yield sub-optimal results. We propose a new, computational approach that allows estimation of spatial resolution directly from the PET image itself. We generalized the so-called logarithmic intensity plots method to the 3D case to perform a spatial resolution estimation in both axial and in-plane directions of the PET images. The proposed approach was applied to two different cohorts. The first cohort consisted of [18F]florbetapir amyloid PET images and matching phantoms coming from a Phase II clinical trial and includes different scanner models and/or orientation and grid reconstructions. The second cohort included β-amyloid, FDG and tau PET images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We obtained in-plane and axial resolution estimators that vary between 3.5 mm and 8.5 mm for both PET and matching phantom images. In both cases, we obtained small across-subject variability in groups of images sharing the same PET scanner model and reconstruction parameters. For human PET images, we also obtained a strong cross-tracer and longitudinal consistency in the spatial resolution estimators. Our novel approach does not only eliminate the need for surrogate brain phantom data, but also provides a general framework that can be applied to a wide range of tracers and other image modalities, such as SPECT.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".