Kinetic model‐based factor analysis of dynamic sequences for 82‐rubidium cardiac positron emission tomography
Bibliographic record
Abstract
PURPOSE: Factor analysis has been pursued as a means to decompose dynamic cardiac PET images into different tissue types based on their unique temporal signatures to improve quantification of physiological function. In this work, the authors present a novel kinetic model-based (MB) method that includes physiological models of factor relationships within the decomposition process. The physiological accuracy of MB decomposed (82)Rb cardiac PET images is evaluated using simulated and experimental data. Precision of myocardial blood flow (MBF) measurement is also evaluated. METHODS: A gamma-variate model was used to describe the transport of (82)Rb in arterial blood from the right to left ventricle, and a one-compartment model to describe the exchange between blood and myocardium. Simulations of canine and rat heart imaging were performed to evaluate parameter estimation errors. Arterial blood sampling in rats and (11)CO blood pool imaging in dogs were used to evaluate factor and structure accuracy. Variable infusion duration studies in canine were used to evaluate MB structure and global MBF reproducibility. All results were compared to a previously published minimal structure overlap (MSO) method. RESULTS: Canine heart simulations demonstrated that MB has lower root-mean-square error (RMSE) than MSO for both factor (0.2% vs 0.5%, p < 0.001 MB vs MSO, respectively) and structure (3.0% vs 4.7%, p < 0.001) estimations, as with rat heart simulations (factors: 0.2% vs 0.9%, p < 0.001 and structures: 3.0% vs 6.7%, p < 0.001). MB blood factors compared to arterial blood samples in rats had lower RMSE than MSO (1.6% vs 2.2%, p =0.025). There was no difference in the RMSE of blood structures compared to a (11)CO blood pool image in dogs (8.5% vs 8.8%, p =0.23). Myocardial structures were more reproducible with MB than with MSO (RMSE=3.9% vs 6.2%, p < 0.001), as were blood structures (RMSE=4.9% vs 5.6%, p =0.006). Finally, MBF values tended to be more reproducible with MB compared to MSO (CV= 10% vs 18%, p =0.16). The execution time of MB was, on average, 2.4 times shorter than MSO (p < 0.001) due to fewer free parameters. CONCLUSIONS: Kinetic model-based factor analysis can be used to provide physiologically accurate decomposition of (82)Rb dynamic PET images, and may improve the precision of MBF quantification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".