Comparison of FORE, OSEM and SAGE algorithms to 3DRP in 3D PET using phantom and human subject data
Bibliographic record
Abstract
Using phantom and human subject data, the authors have compared a number of reconstruction algorithms to the current "gold standard" method, 3D Reprojection Method (3 DRP), in an effort to validate them as practical 3D reconstruction alternatives for dynamic PET scanning. The algorithms evaluated were (a) Fourier Rebinning (FORE) followed by 2D Filtered Back Projection (2D FBP), (b) FORE followed by Ordered Subsets Expectation-Maximization (OSEM) and (c) FORE followed by Space Alternating Generalized Expectation-Maximization (SAGE). The main benefits of these methods are two-fold: significantly shorter reconstruction times and improved signal-to-noise ratio at matched resolution for the iterative schemes. The authors demonstrate that FORE+2D FBP can replace 3 DRP with no significant impact on subsequent data analysis. In particular, distribution volume ratios (DVRs) obtained with FORE+2D FBP differed from those obtained with 3 DRP by 0.3/spl plusmn/0.7% (n=33) and -0.7/spl plusmn/1.1% (n=27) for cortical and cerebellar input functions, respectively. Both OSEM and SAGE performed well compared to 3 DRP, with improved noise characteristics and DVR differences of <3%.
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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 | 0.007 | 0.016 |
| 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 | 0.001 | 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".