Virtual Reality Visualization of CFD Simulated Blood Flow in Cerebral Aneurysms Treated with Flow Diverter Stents
Bibliographic record
Abstract
Virtual reality (VR) has the potential to be a powerful tool for the visualization of simulated blood flow in cerebral aneurysms. This paper presents our study aimed at developing the VR visualization of computational fluid dynamics (CFD) simulations of cerebral aneurysms treated with flow-diverting (FD) stents. First, a spherical sidewall aneurysm located at a simplified internal carotid artery was considered for investigating the impact of stent deployment and positioning on the corresponding spatially time-varying blood flow behavior. The three-dimensional unsteady blood flow over a cardiac cycle was simulated numerically using the finite volume method, and the distributions of hemodynamic parameters inside the aneurysm sac, and on its wall, were presented with and without stent cases. Two stent positions, with and without a gap between the artery wall and stent, were considered to show the influence of correct stent position on aneurysm treatment. Second, a straightforward workflow was developed to import, process, and visualize the CFD analysis data in a VR environment by using open-source software with a high resolution. The Unity3D engine was used for displaying the processed animations in a VR environment operated on a head-mounted display (HMD). The refining process of each frame of time-varying CFD data was automated. The animated flow elements rendered in the VR environment were velocity vectors, velocity contours, streamlines, particle traces, and point clouds. CFD results showed that proper stenting facilitates thrombosis and occlusion of the aneurysm by modification of the flow patterns, which leads to lower inflow jet velocities into the aneurysm, longer turnover time, lower aneurysm-averaged kinetic energy, and lower wall shear stress. Additionally, the results indicated that a gap between the stent and the parent artery may lead to undesirable hemodynamic alterations. The VR visualization illustrated that the recognition of the potential in danger regions of aneurysms and the evaluation of the performance of FD stents in aneurysm treatment can be conducted without the need for several slices through the parent artery and aneurysm, as is required for traditional postprocessing methods. Through VR visualization, the details of the simulation results become readily available by navigating in the 3D animated flow elements using a high-degree-of-freedom headset.
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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 | 0.001 |
| 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".