Non-Newtonian Blood Modeling in Intracranial Aneurysm Hemodynamics: Impact on the Wall Shear Stress and Oscillatory Shear Index Metrics for Ruptured and Unruptured Cases
Bibliographic record
Abstract
When simulating blood flow in intracranial aneurysms (IAs), the Newtonian model seems to be ubiquitous. However, analyzing the results from the few studies on this subject, the doubt remains on whether it is necessary to use non-Newtonian models in computational fluid dynamics (CFD) simulations of cerebral vascular flows. The objective of this study is to investigate whether different rheology models would influence the hemodynamic parameters related to the wall shear stress (WSS) for ruptured and unruptured IA cases, especially because ruptured aneurysms normally have morphological features, such as lobular regions and blebs, that could trigger non-Newtonian phenomena in the blood flow due to low shear rates. Using CFD in an open-source framework, we simulated four ruptured and four unruptured patient-specific aneurysms to assess the influence of the blood modeling on the main hemodynamic variables associated with aneurysm formation, growth, and rupture. Results for WSS and oscillatory shear index (OSI) and their metrics were obtained using Casson and Carreau-Yasuda non-Newtonian models and were compared with those obtained using the Newtonian model. We found that all differences between non-Newtonian and the Newtonian models were consistent among all cases irrespective of their rupture status. We further found that the WSS at peak systole is overestimated by more than 50% by using the non-Newtonian models, but its metrics based on time and surface averaged values are less affected-the maximum relative difference among the cases is 7% for the Casson model. On the other hand, the surface-averaged OSI is underestimated by more than 30% by the non-Newtonian models. These results suggest that it is recommended to investigate different blood rheology models in IAs simulations when specific parameters to characterize the flow are needed, such as peak-systole WSS and OSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".