快速虚拟分数流量储备使用3D计算流体动力学
Thomas Newman1,2, Raunak Borker3, Louise Aubiniere-Robb1
1Department of Infection, Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Beech Hill Road, Sheffield, S10 2RX, UK.
European heart journal. Digital health
|August 4, 2023
概括
一种新的图形处理单元 (GPU) 方法显著加速了使用3D计算流体动力学 (CFD) 的虚拟分量流量储备 (vFFR) 计算. 这种基于GPU的vFFR速度高达比中央处理器 (CPU) 方法的28倍,准确度可比.
科学领域:
- 心血管成像和干预方法
- 计算流体动力学的流体动力学.
- 医疗技术 医疗技术 医学技术
背景情况:
- 虚拟分量流量储备 (vFFR) 增强了分量流量储备 (FFR) 评估,以指导冠状动脉干预.
- 目前用于vFFR的3D计算流体动力学 (CFD) 方法虽然准确,但计算密集且耗时.
研究的目的:
- 评估一种新的3D-CFD软件方法,利用图形处理单元 (GPU) 对vFFR的计算.
- 为了比较基于GPU的方法的速度,准确性和成本效益与目前最快的基于中央处理器 (CPU) 的3D-CFD技术.
主要方法:
- 研究了一种基于GPU的新3D-CFD软件,用于vFFR计算.
- 将GPU方法与基于CPU的3D-CFD技术进行比较,使用40个血管图案.
- 评估指标包括计算时间,准确性,硬件成本和能源消耗.
主要成果:
- GPU 模拟速度明显更快,计算时间中位数为 31.7 秒,而 CPU 方法为 607.5 秒 (P < 0.0001).
- 新的GPU技术表现出高精度,与CPU方法相比,实现了99.6%的相对精度.
- 虽然GPU的初始硬件成本较高 (4080英 vs. 2876英),但每个案例的中位数能耗明显较低 (8.44Wh vs. 2.60Wh,P < 0.0001).
结论:
- 基于GPU的新3D-CFD方法在vFFR计算中提供高达28倍的加速.
- 这种加快的vFFR计算实现了这种速度的提高,而不会对准确度做出临床显著的妥协.
- 这些发现表明,在临床实践中对vFFR分析有一个更有效和潜在的成本效益的方法.
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