Perfusion parameters derived from bolus‐tracking perfusion imaging are immune to tracer recirculation
Bibliographic record
Abstract
PURPOSE: To investigate the impact of tracer recirculation on estimates of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). MATERIALS AND METHODS: The theoretical model used to derive CBF, CBV, and MTT was examined. CBF and CBV estimates with and without tracer recirculation were compared in computer simulations to examine the effects of tracer recirculation. RESULTS: The equations used to derive CBF, CBV, and MTT assume that the arterial input function and tissue tracer signals define the input and output signals, respectively, of a linear time-invariant system. As a result of the principle of superposition, these perfusion parameters are immune to tracer recirculation, which was confirmed by computer simulation. However, limited acquisition durations can lead to CBV and CBF errors of up to 50%. CONCLUSION: Tracer recirculation does not impact estimation of CBF, CBV, or MTT. However, previous approaches used to remove recirculation effects may be beneficial when used to compensate for limited acquisition durations in which the passage of the bolus is not adequately captured.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".