Input functions extraction from gated <sup>18</sup>F-FDG PET images
Bibliographic record
Abstract
Derivation of the plasma time-activity curve in small animal positron emission tomography (PET) studies is a challenging task. Non-invasive input functions (IF) estimation in cardiac imaging usually involves drawing a region of interest (ROI) within the left ventricle (LV) of the heart. The small size of the LV relative to the resolution of the small-animal PET system, coupled with spillover and heart motion, makes this method difficult. In this work, we acquired rat cardiac images in list-mode with 16 ECG-gates with PET and18F-fluorodeoxyglucose (FDG). We introduced a customized coupled active contour model to reduce the image contamination from blood to tissue and from tissue to blood which are due to organ movements and spillover. The new findings were that we added an external energy to the internal contour to consider the contrast blood-to-tissue as important as the contrast tissue-to-outside myocardium. In order to correct the blood and tissue regions for spillover, we decomposed the two dynamic ROIs in their blood and tissue components using Bayesian probabilities. The results showed a good separation of the blood and tissue components in images as compared to the external blood sampling.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".