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Lattice-Boltzmann interactive blood flow simulation pipeline.

Sahar S Esfahani1, Xiaojun Zhai2, Minsi Chen3

  • 1College of Engineering, Qatar University, Doha, Qatar.

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|March 5, 2020
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Summary

This study presents a new pipeline for simulating and visualizing cerebral blood flow, improving speed and performance for better detection and treatment of brain aneurysms. The enhanced system offers real-time insights into blood flow dynamics, aiding clinical decisions.

Keywords:
Cerebral aneurysmGPULattice-BoltzmannPipelineVisualization

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Area of Science:

  • Biomedical Engineering
  • Computational Fluid Dynamics
  • Medical Imaging

Background:

  • Cerebral aneurysms are common cerebrovascular disorders caused by weakened brain arteries, posing significant clinical challenges for accurate detection and treatment.
  • Current interventional radiology treatments for brain aneurysms heavily rely on radiologist expertise, highlighting the need for advanced diagnostic and therapeutic tools.

Purpose of the Study:

  • To introduce a comprehensive pipeline for cerebral blood flow simulation and real-time visualization.
  • To address the clinical challenges in accurate detection and effective therapy of cerebral aneurysms.

Main Methods:

  • Utilized an improved version of HemeLB, a parallel lattice-Boltzmann fluid solver, as the computational core.
  • Implemented a CUDA-based GPU ray marching method for the visualization component.
  • Integrated medical image acquisition to real-time visualization and steering.

Main Results:

  • The developed visualization engine demonstrated superior scalability and update rates compared to the original HemeLB.
  • Achieved more than two times the speed of the original system.
  • Enabled 3D visualization processing at over 30 frames per second.

Conclusions:

  • Presented a reliable environment for in vivo blood flow modeling and visualization in cerebral aneurysms.
  • The pipeline enhances visualization speed and processing unit performance through task decomposition and GPU utilization.
  • The proposed system can be integrated into clinical routines for real-time cerebral blood flow information delivery to clinicians.