Simultaneously Improving Accuracy and Precision within Dynamic Kernelized PET Reconstruction
Bibliographic record
Abstract
We propose two kernelized reconstruction methods for simultaneously improving accuracy and precision for dynamic PET imaging, followed by validations using 4D simulations. One of the proposed methods utilizes an effective kernel matrix which consists of an accuracy component that accounts for partial volume effect and a precision component which achieves 4D noise reduction (i.e. RBV-HYPR4D-K-OSEM), while the other method incorporates the standard resolution modeling within our previously proposed 4D de-noised reconstruction method (i.e. PSF-HYPR4D-K-OSEM). It was observed that the inclusion of the accuracy kernel improves the convergence rate in contrast recovery coefficient (CRC) for relatively small regions, whereas the inclusion of resolution modeling slows down the convergence rate. As compared to the standard OSEM with post filter, both proposed methods achieved better CRC vs noise trade off and mean absolute percent error across the time-activity curves, while RBV-HYPR4D outperforms PSF-HYPR4D in terms of accuracy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".