Increased precision in the intravascular arterial input function with flow compensation
Bibliographic record
Abstract
PURPOSE: In this study, we investigate the effects of pulsatile flow and inflow on dynamic susceptibility-contrast MRI intravascular arterial input function measurement in human brain arteries and measure how they are affected by first-order flow compensation. METHODS: A dual-echo single-shot EPI sequence with alternating flow compensation gradients was used to acquire dynamic susceptibility-contrast images with electrocardiogram monitoring. The dynamic signal variations measured inside the middle cerebral and internal carotid arteries were associated to the pulsatile arterial blood velocities measured with a single-slice quantitative flow sequence throughout the cardiac cycle. RESULTS: Major inverse correlations between intravascular signal and blood velocity were found for the standard single-shot EPI sequence. Flow compensation reduces these correlated variations that contribute to signal physiological noise. This causes a significant twofold increase of intravascular SNR in the middle cerebral and the internal carotid arteries (2.3 ± 0.9, P = 0.03) and (2.0 ± 0.9, P = 0.04), respectively; and reduced phase SD for the internal carotid arteries (0.72 ± 0.14, P = 0.004). The correction proposed in this work translates into a quantitative arterial input function with reduced noise in the internal carotid arteries. CONCLUSION: The physiological noise added by pulsatile flow and inflow for intravascular arterial input function measurement in the brain arteries is significantly reduced by flow compensation.
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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 | 0.009 |
| 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 | 0.001 |
| 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".