Cross-validation stopping rule for ML-EM reconstruction of dynamic PET series: effect on image quality and quantitative accuracy
Bibliographic record
Abstract
A major shortcoming of the Maximum Likelihood Expectation Maximization (ML-EM) method for reconstruction of dynamic PET images is to decide when to stop the iterative process for image frames with largely different statistics and activity distributions. A widespread practice to overcome this problem involves over-iteration of an image estimate followed by smoothing. In this work, the authors investigate the qualitative and quantitative accuracy of the cross-validation procedure (CV) as a stopping rule, in comparison to over-iteration and post-filtering. For the reconstruction of phantom and small animal dynamic FDG-PET data acquired in 2-D mode. The CV stopping rule ensured visually acceptable image estimates with balanced resolution and noise characteristics. However, quantitative accuracy required more than 10/sup 5/ events per image. The effect of the number of ML-EM iterations on time-activity curves and metabolic rates of glucose extracted from image series is discussed. A dependence of the CV defined number of iterations on projection counts was found which simplifies reconstruction and reduces computation time.
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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 | 0.056 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".