Image-based computational simulation of flow dynamics in a giant intracranial aneurysm.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Blood flow dynamics are thought to play an important role in the pathogenesis and treatment of intracranial aneurysms; however, hemodynamic quantities of interest are difficult to measure in vivo. This study shows that computational fluid dynamics (CFD) combined with computed rotational angiography can provide such hemodynamic information in a patient-specific and prospective manner. METHODS: A 58-year-old woman presented with partial right IIIrd cranial nerve palsy due to a giant carotid-posterior communicating artery aneurysm that was subsequently coiled. Computed rotational angiography provided high resolution volumetric image data from which the lumen geometry was extracted. This and a representative flow rate waveform were provided as boundary conditions for finite element CFD simulation of the 3D pulsatile velocity field. RESULTS: CFD analysis revealed high speed flow entering the aneurysm at the proximal and distal ends of the neck, promoting the formation of both persistent and transient vortices within the aneurysm sac. This produced dynamic patterns of elevated and oscillatory wall shear stresses distal to the neck and along the sidewalls of the aneurysm. These hemodynamic features were consistent with patterns of contrast agent wash-in during cine angiography and with the configuration of coil compaction observed at 6-month follow-up. CONCLUSION: Anatomic realism of lumen geometry and flow pulsatility is essential for elucidating the patient-specific nature of aneurysm hemodynamics. Such image-based CFD analysis may be used to provide key hemodynamic information for prospective studies of aneurysm growth and rupture or to predict the response of an individual aneurysm to therapeutic options.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".