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Lattice-Boltzmann interactive blood flow simulation pipeline.

Esfahani, Sahar S and Zhai, Xiaojun and Chen, Minsi and Amira, Abbes and Bensaali, Faycal and AbiNahed, Julien and Dakua, Sarada and Younes, Georges and Baobeid, Abdulla and Richardson, Robin A and Coveney, Peter V (2020) 'Lattice-Boltzmann interactive blood flow simulation pipeline.' International Journal of Computer Assisted Radiology and Surgery, 15 (4). 629 - 639. ISSN 1861-6410

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Abstract

PURPOSE:Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering. METHODS:We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores. RESULTS:The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second. CONCLUSION:A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 22 Apr 2020 10:40
Last Modified: 22 Apr 2020 11:15
URI: http://repository.essex.ac.uk/id/eprint/27198

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