Simultaneous Perfusion-Metabolism Imaging with 18F-FDG Positron Emission Tomography
Bibliographic record
Abstract
Perfusion-glucose metabolism imaging with positron emission tomography (PET) has utility in cancer among other diseases in identifying a mismatch between vascular delivery of glucose and its utilization in tissue. PET perfusion-metabolism requires serial imaging with a flow radiotracer and18F-fluorodeoxyglucose (FDG), which is resource intensive. We hypothesized that perfusion and glucose metabolism can be simultaneously estimated with a flow-modified two-tissue compartment kinetic model (F2TCM) applied to FDG-PET studies. The F2TCM is a time-domain approximation to a distributed parameter (DP) model without a closed-form time domain solution. A basis function and exponential spectral analysis-based method were used to estimate the seven F2TCM parameters. We validated the F2TCM by estimating parameters from synthetic tissue time-activity curves generated with the DP model over 1024 trials of PET-like noise simulations. The mean ± standard deviation bias in estimated perfusion versus the ground truth over 1024 noise trials and ten DP parameter sets was -6.1 ± 5.7% with an average coefficient of variation of 24.6%. Bias and variation were likely from differences between the F2TCM and the DP model and lower signal-to-noise ratio, the latter which is partially mitigated in patient studies as dynamic images and maps are strongly filtered. Applying the F2TCM to a total body FDG-PET study of a patient with metastatic lung cancer, we observed a lung tumor with reduced perfusion and increased net uptake rate (Ki), indicating a mismatch between vascular delivery of glucose via perfusion versus its utilization and tumor energy demand. Parameter decrease was unique to perfusion as blood volume and K1(perfusion surrogates) were normal or elevated. Our novel contribution was to demonstrate the feasibility of perfusion-metabolism imaging from a single FDG-PET study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".