Spectral decomposition and illustration-inspired visualisation of highly disturbed cerebrovascular blood flow dynamics
Bibliographic record
Abstract
Informed by high-resolution computational fluid dynamics (CFD) simulations, we present a strategy using a temporal filtering approach to examine the three-dimensional structures of velocity fluctuations filtered based on the global spectral content. Research suggests the presence of transient, turbulent-like flow instabilities at a range of frequencies from 10 Hz up to 1 kHz, some of which are associated with clinical reports of aneurysm vibration or ‘bruits’, and which may promote aneurysm growth or rupture. To isolate and visualise these instabilities with respect to their frequency, the filtering technique presented in this work is applied to the flow simulations of three middle cerebral artery (MCA) aneurysms and three internal carotid artery (ICA) siphons. Vortex cores associated with the different frequency bands are then visualised together to highlight their spatiotemporal interactions. Inspired by visual styles of illustration, we present a rendering strategy depicted with outlines, silhouettes and two-tone cel-shading to prevent occlusion and emphasise the resulting flow structures of importance while the other details are given less weight to de-emphasise their presence in the background of the image plane. Reinforcing previous studies in the literature, the current work also confirmed the presence of flow fluctuations to the order of up to 1 kHz when modelled adequately using high-resolution CFD simulations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".