P-016 Lagrangian trajectory simulations with synchrotron microtomography improves hemodynamic analysis of intracranial aneurysms treated with flow diverting stents
Bibliographic record
Abstract
Background Successful treatment of cerebral aneurysms with flow diverting stents (FDS) depends upon altering biomechanical forces to precipitate stable thrombus formation. Computational fluid dynamics (CFD) simulations can model such forces for treatment outcome prediction, and particle tracking methods can simulate the behavior of components of blood such as platelets. Previous work using a porous medium (PM) to simulate the stent surface demonstrated changes in particle tracking metrics following treatment.(1) However, the fidelity of the PM representation of the stent (and its effect on intraaneurysmal blood flow) has not been established, and previous studies of PM methods for aneurysm coils suggest the potential for inaccuracies.(2) Thus, the goal of this study is to perform CFD and particle tracking in simulations of patient-specific aneurysms before and after FDS treatment, and evaluate the accuracy of the PM method compared to actual FDS geometry. Method Data were collected from patients with unruptured intracranial aneurysms treated with FDS. Three-dimensional reconstructions of patient blood vessels were created from rotational angiography and segmented for CFD simulations. A porous layer with face permeability, pressure-jump coefficient, and thickness corresponding to the geometry of the FDS was applied across the aneurysm neck in each patient. To provide a ground truth, each patients’ segmented arterial anatomy was used to 3D-print optically-clear silicone phantoms. Each phantom was treated with an FDS that was deployed by an experienced neurosurgeon and replicated the patient’s in vivo treatment. Phantoms were then imaged by microtomography to obtain high resolution volumetric reconstructions of the implanted FDS. Simulations were run for both the PM and microtomography FDS conditions in each patient, with patient-specific boundary conditions derived from endovascular measurements of blood flow and velocity obtained before and after treatment. Massless particles injected into these simulations were individually tracked to calculate particle tracking metrics, such as residence time and shear stress history. The hemodynamic results were compared between the PM and microtomographic simulations. Results Nine subjects have undergone microtomography, with a goal of 25 subjects total for the study. We anticipate that the PM method will exaggerate the effects of treatment on residence time and shear stress history in comparison to the ground truth microtomography simulations. The inaccuracy of the PM method will be quantified, and corrective factors derived for application in future CFD studies. Conclusions Appreciating how endovascular therapies alter fluid biomechanics is critical to understanding what hemodynamic changes are needed to achieve treatment success. Improving the accuracy of the computational representation of endovascular treatment devices will increase the confidence and clinical utility of these methods. 1. Marsh LMM, Barbour MC, Chivukula VK, Chassagne F, Kelly CM, Levy SH, et al. Platelet Dynamics and Hemodynamics of Cerebral Aneurysms Treated with Flow-Diverting Stents. Ann Biomed Eng. 2019;September. 2. Chivukula VK, Marsh L, Chasagne F, Barbour M, Kelly C, Levy S, et al. Lagrangian Trajectory Simulation of Platelets and Synchrotron Microtomography Augment Hemodynamic Analysis of Intracranial Aneurysms Treated with Embolic Coils. J Biomech Eng. 2021;July. Disclosures D. Bass: None. L. Marsh: None. V. Chivukula: None. M. Barbour: None. P. Fillingham: None. L. Kim: None. A. Aliseda: None. M. Levitt: 1; C; Medtronic: Investigator-initiated unrestricted educational grant, Stryker: Investigator-initiated unrestricted educational grant. 2; C; Medtronic, Stereotaxis. 4; C; Fluid Biomed. 5; C; Metis Innovative, Aeaean Advisors. 6; C; JNIS and Frontiers in Surgery Editorial Boards.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".