Platelet simulations to enhance computational fluid modeling of intracranial aneurysm coil embolization

ORAL

Abstract

Intracranial aneurysms can rupture causing a devastating hemorrhagic stroke. An increasingly preferable treatment to prevent rupture is minimally-invasive endovascular coil embolization, which utilizes metallic coils to fill the aneurysm dome, occlude blood flow inside the dome, and promote the formation of a stable thrombus. Eulerian computational fluid dynamics has allowed for greater understanding of aneurysm hemodynamics, but these methods don’t fully characterize the platelet microenvironment and platelet activation that is critical for progressive thrombosis and subsequent aneurysm healing. Platelet activation is known to be associated with shear stress and particularly with platelet accumulation in areas of prolonged residence time. We apply novel Langrangian computational modeling to patient-specific aneurysmal vasculature before and after treatment with embolic coils to characterize both hemodynamic and platelet microenvironment variables, resulting in significantly more accurate prediction of treatment outcomes. Establishing clinically-useful metrics to quantify the increased proportion of platelets having a thrombogenic residence time will inform future treatment approaches, thus mitigating the risk of hemorrhagic stroke.

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Presenters

  • Cory M. Kelly

    Department of Neurological Surgery, University of Washington, Seattle, WA, USA

Authors

  • Cory M. Kelly

    Department of Neurological Surgery, University of Washington, Seattle, WA, USA

  • Laurel Morgan Miller Marsh

    Department of Mechanical Engineering, University of Washington, Seattle, WA, USA

  • Michael C. Barbour

    Department of Mechanical Engineering, University of Washington, Seattle, WA, USA

  • Fanette Chassagne

    Department of Mechanical Engineering, University of Washington, Department of Mechanical Engineering, University of Washington, Seattle, WA, USA, University of Washington, Department of Mechanical Engineering

  • Venkat Keshav Chivukula

    Department of Mechanical Engineering, University of Washington, Department of Mechanical Engineering, University of Washington, Seattle, WA, USA, University of Washington, Department of Mechanical Engineering, University of Washington

  • Samuel H. Levy

    Department of Neurological Surgery, University of Washington, Seattle, WA, USA

  • Michael R. Levitt

    Department of Neurological Surgery, University of Washington, Department of Mechanical Engineering, University of Washington, Department of Neurological Surgery, University of Washington, Seattle, WA, USA, Department of Mechanical Engineering, University of Washington, Seattle, WA, USA

  • Louis J. Kim

    Department of Neurological Surgery, University of Washington, Department of Neurological Surgery, University of Washington, Seattle, WA, USA, Department of Neurological Surgery, University of Washington, Seattle, WA, USA, Department of Radiology, University of Washington, Seattle, WA, USA

  • Alberto Osuna Aliseda

    University of Washington, Department of Mechanical Engineering, University of Washington, Department of Mechanical Engineering, University of Washington, Seattle, WA, USA, Department of Neurological Surgery, University of Washington, Seattle, WA, USA, Mechanical Engineering Department, University of Washington, Department of Mechanical Engineering, University of Washington, Seattle, WA, USA, Mechanical Engineering, University of Washington, Seattle, USA