Reexamining the quantification of perfusion MRI data in the presence of bolus dispersion
Bibliographic record
Abstract
PURPOSE: To determine the true impact of dispersion upon cerebral blood flow (CBF) quantification by removing an algorithm implementation-induced systematic error. MATERIALS AND METHODS: The impact of dispersion on the arterial input function (AIF) between measurement and entry into the tissue of interest on CBF estimates was simulated assuming: 1) contralateral circulation flow that introduces a true arterial tissue delay (ATD)-related dispersive component; and 2) the presence of an arterial stenosis that disperses and shifts the AIF peak entering the tissue; increasing the apparent ATD relative to the original AIF. RESULTS: Previously reported CBF estimates for the stenosis dispersion model were found to be a mixture of true dispersive effects and an algorithm implementation-induced systematic error. The true CBF(MEASURED)/CBF(NO-DISPERSION) ratios for short mean transit times (MTT) (normal) and long MTT (infarcted) tissue were similar for both dispersion models evaluated; this was an unanticipated result. The CBF quantification inaccuracies induced through the dispersion model truly related to ATD were lower than for the local stenosis-based dispersion for small ATD values. CONCLUSION: Correcting the systematic error present in a previous deconvolution study removes the reported ATD-related impact on CBF quantification. The impact of dispersion was smaller than half that reported in previous simulation 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".