Comparison between an image- and a sinogram-based correction algorithm for partial volume effect in 3D PET imaging
Bibliographic record
Abstract
Two fully 3D partial volume correction (PVC) techniques in PET imaging are compared. They follow the region based method proposed in 2D by O. Rousset et al. (1998). They assume that the object being imaged consists of anatomical domains with homogeneous true activity and that the voxel intensity in the PET image is the sum of the true activity in each domain weighted by its regional spread function (RSF). The two implementations that we compare differ in the way the RSFs are obtained: (1) a 3D extension of the original work of Rousset, that is based on an analytical simulator, and (2) a convolution of the anatomical tissue domains, in the image space, with the 3D PET system PSF. We used a Monte Carlo simulated cerebral dynamic study to assess the performance of both PVC implementations in the recovery of the time activity curves for the striata. The two methods allow the recovery of the true time activity curves with RMS errors of about 4%. The advantage of the second approach is its simplicity and rapidity that would enable fully 3D PVC in a clinical context, for protocols dedicated to compartmental analysis that require a few accurate ROI time activity curves.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".