College of Engineering, Qatar University, Doha, Qatar.
School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Int J Comput Assist Radiol Surg. 2020 Apr;15(4):629-639. doi: 10.1007/s11548-020-02120-3. Epub 2020 Mar 4.
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.
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.
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.
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.
脑动脉瘤是全世界成年人中常见的脑血管疾病之一，是由脑动脉薄弱引起的。脑动脉瘤最令人印象深刻的治疗方法是介入放射学治疗，这种治疗方法极其依赖放射科医生的技能水平。因此，准确检测和有效治疗脑动脉瘤仍然是重要的临床挑战。在这项工作中，我们引入了一个从医学图像采集到实时可视化和引导的脑血流模拟和实时可视化的管道。
我们开发并采用了改进版的 HemeLB 作为管道的主要计算核心。HemeLB 是一种针对稀疏和复杂几何形状进行了优化的大规模并行晶格玻尔兹曼流体求解器。该管道的可视化组件基于在 CUDA 功能 GPU 内核上实现的光线追踪方法。
对所提出的可视化引擎进行了全面评估，报告的结果表明，与原始的 HemeLB 相比，它实现了更高的可扩展性和每秒更新的站点数量，这表明了几何站点值的更高更新率。该提出的引擎速度快两倍以上，能够以每秒处理 30 多帧的速度进行 3D 可视化。
本工作提出了一种用于测量和显示体内血流模式的可靠建模和可视化环境，可以深入了解脑动脉瘤的血液动力学特征。该管道通过将任务分解为更小的任务并与 GPU 协作来渲染图像，提高了可视化的速度，并最大限度地提高了处理单元的性能。因此，所提出的管道可以作为临床常规的一部分应用，为临床医生提供实时的与脑血流相关的信息。
