Improving accuracy for finite element modeling of endovascular coiling of intracranial aneurysm

Abstract

BackgroundComputer modeling of endovascular coiling intervention for intracranial aneurysm could enable a priori patient-specific treatment evaluation. To that end, we previously developed a finite element method (FEM) coiling technique, which incorporated simplified assumptions. To improve accuracy in capturing real-life coiling, we aimed to enhance the modeling strategies and experimentally test whether improvements lead to more accurate coiling simulations.MethodsWe previously modeled coils using a pre-shape based on mathematical curves and mechanical properties based on those of platinum wires. In the improved version, to better represent the physical properties of coils, we model coil pre-shapes based on how they are manufactured, and their mechanical properties based on their spring-like geometric structures. To enhance the deployment mechanics, we include coil advancement to the aneurysm in FEM simulations. To test if these new strategies produce more accurate coil deployments, we fabricated silicone phantoms of 2 patient-specific aneurysms in duplicate, deployed coils in each, and quantified coil distributions from intra-aneurysmal cross-sections using coil density (CD) and lacunarity (L). These deployments were simulated 9 times each using the original and improved techniques, and CD and L were calculated for cross-sections matching those in the experiments. To compare the 2 simulation techniques, Euclidean distances (dMin, dMax, and dAvg) between experimental and simulation points in standardized CD-L space were evaluated. Univariate tests were performed to determine if these distances were significantly different between the 2 simulations.ResultsCoil deployments using the improved technique agreed better with experiments than the original technique. All dMin, dMax, and dAvg values were smaller for the improved technique, and the average values across all simulations for the improved technique were significantly smaller than those from the original technique (dMin: p = 0.014, dMax: p = 0.013, dAvg: p = 0.045).ConclusionIncorporating coil-specific physical properties and mechanics improves accuracy of FEM simulations of endovascular intracranial aneurysm coiling.

Aneurysm characteristics, coil packing, and post-coiling hemodynamics affect long-term treatment outcome

“…In addition to inherent aneurysm characteristics and the degree of coil packing, long-term outcome is also influenced by the flow modifications by coils. To characterize how the coils alter the flow in each IA case, we used our recently-validated FEM coiling technique, which is among the most realistic modeling techniques as it explicitly models physical coil properties and coil deployment mechanics 8 9. Because it models coils explicitly, our technique enabled the investigation of intra-aneurysmal flow around the coils in each case.…”

“…To recapitulate the specific coil intervention in each case virtually, we used our previously established FEM coiling technique to model coil deployments 8 9. For each case, we created beam element models of the specific coils used in the intervention based on their physical properties, which included their length, diameter, pre-shape diameter, and material properties of their primary wire.…”

“…Then, we recreated the treatment in the aneurysm model using our FEM technique in Abaqus (ver.2016, SIMULIA, Providence, RI). The full technical details of this technique are published elsewhere 8 9…”

“…To test this hypothesis, we retrospectively collected 52 unruptured and ruptured IAs that were treated by primary coiling, created patient-specific computer models from their pre-coiling digital subtraction angiography (DSA) images, calculated aneurysm morphology, and simulated pre-coiling hemodynamics using CFD. We then used our previously established finite element method (FEM) coiling technique to virtually recapitulate coil treatments in these aneurysm and performed post-coiling CFD 8 9. Univariable analysis was performed on 26 morphologic, treatment-specific, and hemodynamic parameters to find significant metrics that could discriminate treatment outcome (occluded vs recurrent) at >1 year follow-up.…”

In Silico Evaluation of Treatment of Periprosthetic Fractures in Elderly Patients After Hip Arthroplasty

“…Other novel applications of FEM extend to the evaluation of the biomechanical properties of the eustachian tube that connects the middle ear to the nasopharynx, which has to do with cartilage stiffness and mucosal adhesion in otitis media (Malik et al 2016). Modeling of coiling deployment during endovascular interventions for intracranial aneurysms (Damiano et al 2019) or the evaluation of a rupture risk of abdominal aortic aneurism (Siika et al 2019;Soto et al 2018) are other examples of this apparently burgeoning field of medicine. Biomechanical fingerprinting of lung tissue perfusion changes underlying inflammatory disorders seems an intriguing possibility to enhance the diagnostics of SARS-CoV-2 pneumonitis and the like.…”