High-resolution cerebral blood flow simulation with a domain decomposition method and verified by the TCD measurement *

Background: An efficient and accurate blood flow simulation can be useful for understanding many vascu-lar diseases. Accurately resolving the blood flow velocity based on patient-specific geometries and model parameters is still a major challenge because of complex geomerty and turbulence issues. In addition, obtaining results in a short amount of computing time is important so that the simulation can be used in the clinical environment. In this work, we present a parallel scalable method for the patient-specific blood flow simulation with focuses on its parallel performance study and clinical verification.Methods: We adopt a fully implicit unstructured finite element method for a patient-specific simulation of blood flow in a full precerebral artery. The 3D artery is constructed from MRI images, and a paral-lel Newton-Krylov method preconditioned with a two-level domain decomposition method is adopted to solve the large nonlinear system discretized from the time-dependent 3D Navier-Stokes equations in the artery with an integral outlet boundary condition. The simulated results are verified using the clin-ical data measured by transcranial Doppler ultrasound, and the parallel performance of the algorithm is studied on a supercomputer. Results: The simulated velocity matches the clinical measured data well. Other simulated blood flow pa-rameters, such as pressure and wall shear stress, are within reasonable ranges. The results show that the parallel algorithm scales up to 2160 processors with a 49% parallel efficiency for solving a problem with over 20 million unstructured elements on a supercomputer. For a standard cerebral blood flow sim-ulation case with approximately 4 million finite elements, the calculation of one cardiac cycle can be finished within one hour with 10 0 0 processors.Conclusion: The proposed method is able to perform high-resolution 3D blood flow simulations in a patient-specific full precerebral artery within an acceptable time, and the simulated results are compara-ble with the clinical measured data, which demonstrates its high potential for clinical applications.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study presents a parallel scalable method for patient-specific blood flow simulation, focusing on parallel performance study and clinical verification. The algorithm achieves 49% parallel efficiency on a supercomputer with over 20 million unstructured elements.