Comparison of Computational Fluid Dynamics with Transcranial Doppler Ultrasound in Response to Physiological Stimuli
Bibliographic record
Abstract
Abstract Cerebrovascular haemodynamics are sensitive to multiple physiological stimuli that require synergistic response to maintain adequate perfusion. Understanding haemodynamic changes within cerebral arteries is important to inform how the brain regulates perfusion, however methods for direct measurement of cerebral haemodynamics in these environments is challenging. The aim of this study was to assess velocity waveform metrics obtained using transcranial Doppler (TCD) with flow conserving subject-specific three-dimensional (3D) simulations relying on computational fluid dynamics (CFD). Twelve healthy participants underwent head and neck imaging with 3 Tesla magnetic resonance angiography. Velocity waveforms in the middle cerebral (MCA) artery were measured with transcranial Doppler ultrasound (TCD) while diameter and velocity was measured using duplex ultrasound in the internal carotid (ICA) and vertebral (VA) arteries to calculate incoming cerebral flow at rest, during hypercapnia and exercise. CFD simulations were developed for each condition, with velocity waveform metrics extracted in the same insonation region as TCD. Exposure to stimuli induced significant changes in cardiorespiratory measures across all participants. Absolute measured TCD velocities were significantly higher than those calculated from CFD (all P < 0.05), and these data were not correlated across conditions (r range 0.030–0.377, all P > 0.05). However, relative changes in velocity from resting levels exhibited significant positive correlations when the distinct techniques were compared (r range 0.577–0.770, all P < 0.05). Our data indicate that whilst absolute measures of cerebral velocity differ between TCD and 3D CFD simulation, physiological changes from resting levels in time-averaged velocity are significantly correlated between these techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".