A novel method for harmonization of PET image spatial resolution without phantoms
Bibliographic record
Abstract
BACKGROUND: Estimation of the spatial resolution in real images is extremely important in several fields, including crystallography, optics, microscopy, and tomography. In human PET imaging, estimating spatial resolution typically involves the acquisition of images from a physical phantom, typically a Hoffman phantom, which poses a logistical burden, especially in large multi-center studies. Indeed, phantom images may not always be readily available, and this method requires constant monitoring of scanner updates or replacements, scanning protocol changes, and image reconstruction guidelines to establish a equivalence with scans acquired from human subjects. METHODS: We propose a new computational approach that allows estimation of spatial resolution directly from human subject PET images. The proposed technique is based on the generalization of the logarithmic intensity plots in the 2D Fourier domain to the 3D case. The spatial resolution of the image is obtained through the estimated coefficients of a multiple linear regression problem having the logarithm of the squared norm of the Fourier transform as dependent variable and the squared 3D frequencies as multiple predictors. RESULTS: The proposed approach was applied to a cohort of subjects consisting of [18F]florbetapir amyloid PET images and matching phantoms from a Phase II clinical trial, and a second cohort including β-amyloid, FDG, and tau PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The resulting in-plane and axial resolution estimators varied between 3.5 mm and 8.5 mm for both PET and matching phantom images. They also yielded less than one voxel size across-subjects variability in groups of images sharing the same PET scanner model and reconstruction parameters. For human PET images, we also proved that the spatial resolution estimators showed: (1) a very high reproducibility, as measured by intraclass correlation coefficients (ICC > 0.985), (2) a strong cross-tracer linear correlations, and (3) a high within-subject longitudinal consistency, as measured by the maximum difference value between pairs of visits from the same subject. CONCLUSIONS: Our novel approach does not only eliminate the need for surrogate phantom data, but also provides a general framework that can be applied to a wide range of tracers and other imaging modalities, such as SPECT. CLINICAL TRIAL DATA: Cognito Therapeutics' OVERTURE clinical trial (NCT03556280, 2021-08-24), https://clinicaltrials.gov/study/NCT03556280 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".