Workflow and Clinical Implementation of a Simulation Method for the Analysis of Hemodynamics and Structural Mechanics of Cerebral Aneurysms
Bibliographic record
Abstract
The treatment of patients with cerebral aneurysms poses multiple challenges to physicians from rupture to treatment risk assessment and their consequences to the patient's health.Risk estimation support is highly required to make the optimum decisions for individual patients.The aim of this study is to develop the workflow of a simulation method for the analysis of hemodynamics and structural mechanics of cerebral aneurysms as well as its possible clinical implementation.Medical imaging data was taken and converted into CAD data (STL format) to use as geometric input for simulations.Fluid-Structure Interaction (FSI) simulations are utilized in the inflow vessel, the outflow vessels as well as a typical aneurysm.Aitken's underrelaxation method is used to ensure convergence between hemodynamics and structural mechanics.The influence of linear and non-linear elastic material properties of the vessel wall as well as the influence of wall thickness reduction in the aneurysm sac are investigated.In this work, two major hemodynamic (wall shear stress and oscillatory shear index) and two structural mechanic quantities (wall displacement and Mises stress) are evaluated and compared between the different models.The linear elastic material model tends to overestimate displacements at high strain rates.The reduction of wall thickness in the aneurysm region shows an increase in wall stresses.In both cases hemodynamic phenomena are not changed, however a tendency of aneurysm growth can be identified.The model is implemented as part of a graphical user interface.With this, it is possible for medical personnel without simulation background to run sophisticated Fluid-Structure interaction simulations with ease and in future to make optimized decisions for patient treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".