Positron emission tomography kinetic modeling algorithms for small animal dopaminergic system imaging
Bibliographic record
Abstract
Small animal positron emission tomography (PET) imaging allows in vivo quantification of lesion- or treatment-induced neurochemical changes in animal models of disease. Important for quantification are the kinetic modeling methods used to determine biologically-relevant parameters of tracer-tissue interaction. In this work, we evaluate modeling algorithms for the dopaminergic tracers (11)C-dihydrotetrabenazine (DTBZ), (11)C-methylphenidate (MP), and (11)C-raclopride (RAC), used to image the dopaminergic system in the unilateral 6-hydroxydopamine lesioned rat model of Parkinson's disease. For the presynaptic tracers, PET measures are compared with autoradiographic binding measurements using DTBZ and [(3)H]WIN 35,428 (WIN). We independently developed a new variant of the tissue-input Logan graphical modeling method, and compared its performance with the simplified Logan graphical method and the simplified reference tissue with basis functions method (SRTM), for region of interest (ROI) averaged time activity curves (TACs) and parametric imaging. The modified graphical method was found to be effectively unbiased by target tissue noise and has advantages for parametric imaging, while all tested methods were equivalent for ROI-averaged data.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".