Total-Body Multiparametric PET with Hightemporal Resolution Imaging and Blood Flow Modeling of Non-Diffusible Radiotracers
Bibliographic record
Abstract
High-temporal resolution imaging (1-2 s/frame) via total-body PET has stimulated new developments to quantify blood flow based on the early vascular transit of PET tracers. Our recent efforts in this direction focused on analyzing early kinetics (first$\approx 2$mins of the dynamic scan). How we can exploit the rich information contained in the full time-activity curve is still unclear. Here we extended the two-tissue compartment model to account for blood flow. This extended model accounts for time delay, vascular transit (blood flow, mean transit time), blood-tissue transport and clearance ($K_{1}, k_{2}$), and target association and dissociation rate constants ($k_{3}, k_{4}$). First, we show the extended model largely improves high-temporal resolution time-activity fitting over standard compartment modeling for${ }^{18} ~\mathrm{F}$-FDG,${ }^{18} ~\mathrm{F}$-fluciclovine,${ }^{18} ~\mathrm{F}$-AraG, and${ }^{18} ~\mathrm{F}$-NaF by the Akaike Information Criteria. Then, we show blood flow estimates quantitatively agreed between${ }^{18}$F-FDG and${ }^{11} \mathrm{C}$-butanol, a reference flow tracer, in volunteers scanned with both tracers. In certain tissues,$K_{1}$estimates differed between models, possibly because the standard assumption that blood behaves as a compartment may not hold at high temporal resolution. Our work establishes methods to extract the rich information provided by total-body PET time-activity curves for total-body multiparametric imaging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".