Large-scale ensemble simulations of biomathematical brain arteriovenous malformation models using graphics processing unit computation

Mika S Jain; Huy M Do; Max Wintermark; Tarik F Massoud

doi:10.1016/j.compbiomed.2019.103416

Large-scale ensemble simulations of biomathematical brain arteriovenous malformation models using graphics processing unit computation

Comput Biol Med. 2019 Oct:113:103416. doi: 10.1016/j.compbiomed.2019.103416. Epub 2019 Aug 27.

Authors

Mika S Jain¹, Huy M Do², Max Wintermark³, Tarik F Massoud⁴

Affiliations

¹ Department of Physics, Stanford University School of Humanities and Sciences, Stanford, CA, USA; Department of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
² Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford, CA, USA; Department of Neurosurgery, Stanford, CA, USA.
³ Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford, CA, USA; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
⁴ Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford, CA, USA. Electronic address: tmassoud@stanford.edu.

PMID: 31494430
DOI: 10.1016/j.compbiomed.2019.103416

Abstract

Background: Theoretical modeling allows investigations of cerebral arteriovenous malformation (AVM) hemodynamics, but current models are too simple and not clinically representative. We developed a more realistic AVM model based on graphics processing unit (GPU) computing, to replicate highly variable and complex nidus angioarchitectures with vessel counts in the thousands-orders of magnitude greater than current models.

Methods: We constructed a theoretical electrical circuit AVM model with a nidus described by a stochastic block model (SBM) of 57 nodes and an average of 1000 plexiform and fistulous vessels. We sampled and individually simulated 10,000 distinct nidus morphologies from this SBM, constituting an ensemble simulation. We assigned appropriate biophysical values to all model vessels, and known values of mean intravascular pressure (P_mean) to extranidal vessels. We then used network analysis to calculate P_mean and volumetric flow rate within each nidus vessel, and mapped these values onto a graphic representation of the nidus network. We derived an expression for nidus rupture risk and conducted a model parameter sensitivity analysis.

Results: Simulations revealed a total intranidal volumetric blood flow ranging from 268 mL/min to 535 mL/min, with an average of 463 mL/min. The maximum percentage rupture risk among all vessels in the nidus ranged from 0% to 60%, with an average of 29%.

Conclusion: This easy to implement biomathematical AVM model, allowed by parallel data processing using advanced GPU computing, will serve as a useful tool for theoretical investigations of AVM therapies and their hemodynamic sequelae.

Keywords: Biomathematical model; Brain AVM; Hemodynamics; Hemorrhage; Network; Nidus; Rupture.

MeSH terms

Brain / blood supply*
Brain / physiopathology*
Computer Simulation*
Female
Hemodynamics*
Humans
Intracranial Arteriovenous Malformations / physiopathology*
Male
Models, Cardiovascular*