Bibliographic record
Abstract
Positron emission tomography (PET) image reconstruction methods inherently introduce inaccuracies in images, especially in dynamic acquisitions, where the frame length is short and/or when the count rate is low. In order to provide an alternate approach to deal with this data analysis problem in PET kinetic modeling, we extracted the kinetic parameters directly from the sinograms. The present work describes modeling /sup 13/N-ammonia in the sinograms obtained in rats with the Sherbrooke small animal PET scanner. Rats were injected with /sup 13/N-ammonia and a set of dynamic scans was acquired for a total of 20 min. Each element of the dynamic sinograms was decomposed into its basis functions using a spectral analysis technique in conjunction with /sup 13/N-ammonia kinetic model. Components that seemed to be representative of homogeneous structures were obtained as well as the kinetic model parameters. Structures were assumed to be associated with a small range of frequencies and were individually reconstructed on that basis. The resulting images were apparently compartments of blood, nonmetabolized, and metabolized ammonia, which were clearly resolved. Moreover, there were two blood components with time activity curves that showed a difference in the occurrence of their peaks. In conclusion, modeling in the sinogram domain allows one to extract a specific component associated with its major kinetic components and to obtain images that may be representative of kinetic parameters.
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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) | 0.000 | 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".