Simulating the effect of venous dispersion on distribution volume measurements from the Logan plot
Bibliographic record
Abstract
Measurement of distribution volume with the Logan plot requires an arterial time activity curve (TAC). The gold standard for measuring arterial activity concentrations is arterial blood sampling. Arterial cannulation carries with it several risks. This work simulated the effects of venous dispersion on measured distribution volumes using the Logan plot with a venous TAC. A representative arterial TAC was selected from a patient study. Simulated tissue TACs were generated from a three compartment kinetic model using the representative arterial TAC. Venous TACs were simulated by convolving the arterial TAC with modified transit time spectra derived from an in vivo dynamic contrast enhanced-CT forearm study. Gaussian noise was added to the tissue TACs. Logan analysis was compared using the arterial and venous TACs for a wide range of kinetic parameters. Bland–Altman analysis was performed to assess agreement between the two methods. Good agreement was observed between values calculated with the arterial and simulated venous TACs for both noiseless and noisy cases. Agreement was slightly dependent on the arterio–venous extraction efficiency of the PET tracer. Results suggest that venous sampling may be a feasible alternative to arterial sampling for analysis with the Logan plot. This technique must be clinically validated in future patient studies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".