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Record W2301888295 · doi:10.1161/str.43.suppl_1.a178

Abstract 178: In-vitro Fluid Dynamic Investigation of a Novel Hyper Elastic-Thin Film Nitinol Stent and the Pipeline Embolization Device for Cerebral Aneurysm Treatment

2012· article· en· W2301888295 on OpenAlexaff
Haithem Babiker, Colin P. Kealey, Youngjae Chun, Greg P. Carman, Daniel S. Levi, David Frakes

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsCégep de Lévis
Fundersnot available
KeywordsPulsatile flowMedicineStentAneurysmBiomedical engineeringFusiform AneurysmParticle image velocimetryFlow velocityPorosityRadiologyMaterials scienceComposite materialMechanicsCardiologyInternal medicineTurbulence

Abstract

fetched live from OpenAlex

Introduction: Fusiform and wide-neck intracranial aneurysms (ICAs) can be challenging to treat with conventional endovascular or surgical means. Recently developed low porosity stents, such as the pipeline embolization device (PED), are designed to treat these ICAs by diverting flow away from the aneurysmal sac. However, low porosity stents rely on a dense metal mesh to divert flow, which reduces flexibility and increases the risk of perforator blockage. In this study, we compare fluid dynamic performance between the PED and a novel high porosity stent fabricated from Hyper Elastic-Thin Film Nitinol (HE-TFN). Methods: An idealized model of a sidewall ICA with a collateral branch, located immediately upstream of the aneurysm, was constructed from transparent silicone. A blood analog solution was circulated through the model at steady and pulsatile flow rates spanning a range of physiologic conditions. The PED and two HE-TFN stents, with pore densities of 8 and 21 pores/mm 2 for 500 and 300 υm pore meshes respectively, were deployed into the model. Each stent was placed across the aneurysm and collateral. Volumetric flow velocity data were acquired before and after stent placement using particle image velocimetry. Computational fluid dynamic (CFD) simulations were also conducted. Results: For both steady and pulsatile conditions, the 300 HE-TFN stent led to the largest drops in RMS Velocity Magnitude in the aneurysmal sac and in cross-neck flow. Under pulsatile conditions, the average drops in aneurysmal RMS Velocity Magnitude over the cardiac cycle, for the range of parent-vessel flow rates investigated, were 42.8-73.7% for the PED, 46.4-58.1% for the 500 HE-TFN, and 68.9-82.7% for the 300 HE-TFN. The largest drops were observed at lower parent-vessel flow rates and at peak systole. Examination of collateral flows showed that the PED led to the largest drops in collateral RMS Velocity Magnitude. Under pulsatile conditions, the average drops in collateral RMS Velocity Magnitude over the cardiac cycle were 38.3-46.1% for the PED, 14.0-25.9% for the 500 HE-TFN, and 34.5-40.8% for the 300 HE-TFN. For steady flow, both the aneurysmal and collateral performance metrics followed similar trends. Conclusion: The 300 HE-TFN stent led to larger reductions in cross-neck flow than the PED. This may be due to the higher pore density of the HE-TFN, which may be of greater importance for aneurysm occlusion than absolute porosity. Additionally, the ultra low profile, thin struts of the HE-TFN reduce perforator blockage as demonstrated by increased post-treatment collateral flows as compared to the PED. Overall, the 300 HE-TFN device performed best among the flow diversion devices examined by this study. CFD results characterizing the flow dynamics of each device will also be presented.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.265
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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