利用三维计算流体动力学快速虚拟分流储备。

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European heart journal. Digital health Pub Date : 2023-04-21 eCollection Date: 2023-08-01 DOI:10.1093/ehjdh/ztad028
Thomas Newman, Raunak Borker, Louise Aubiniere-Robb, Justin Hendrickson, Dipankar Choudhury, Ian Halliday, John Fenner, Andrew Narracott, D Rodney Hose, Rebecca Gosling, Julian P Gunn, Paul D Morris
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摘要

目的:在过去十年中,虚拟分数血流储备(vFFR)提高了分数血流储备(FFR)的实用性,FFR是全球推荐的冠状动脉介入治疗指导评估方法。虽然vFFR的计算速度加快了,但利用全三维计算流体动力学(CFD)解决方案而非简化分析解决方案的技术仍需要大量时间来计算:本研究在 40 个血管造影病例中,对基于图形处理器(GPU)计算的新型 3D-CFD 软件方法的速度、准确性和成本进行了研究,并与现有的基于最快中央处理器(CPU)的 3D-CFD 技术进行了比较。新型 GPU 模拟速度明显快于 CPU 方法(中位数 31.7 秒(四分位数间距 (IQR) 24.0-44.4 秒)vs 607.5 秒(490-964 秒),P < 0.0001)。与 CPU 方法相比,新型 GPU 技术的准确率为 99.6%(IQR 为 99.3-99.9)。GPU 硬件的初始成本高于 CPU(4080 英镑对 2876 英镑),但使用 GPU 方法,每个病例的能耗中值显著降低(8.44 (6.80-13.39) Wh 对 2.60 (2.16-3.12) Wh,P < 0.0001):这项研究表明,使用 3D-CFD 计算 vFFR 的速度比以前的技术最多可加快 28 倍,而且在临床上不会明显牺牲准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Rapid virtual fractional flow reserve using 3D computational fluid dynamics.

Aims: Over the last ten years, virtual Fractional Flow Reserve (vFFR) has improved the utility of Fractional Flow Reserve (FFR), a globally recommended assessment to guide coronary interventions. Although the speed of vFFR computation has accelerated, techniques utilising full 3D computational fluid dynamics (CFD) solutions rather than simplified analytical solutions still require significant time to compute.

Methods and results: This study investigated the speed, accuracy and cost of a novel 3D-CFD software method based upon a graphic processing unit (GPU) computation, compared with the existing fastest central processing unit (CPU)-based 3D-CFD technique, on 40 angiographic cases. The novel GPU simulation was significantly faster than the CPU method (median 31.7 s (Interquartile Range (IQR) 24.0-44.4s) vs. 607.5 s (490-964 s), P < 0.0001). The novel GPU technique was 99.6% (IQR 99.3-99.9) accurate relative to the CPU method. The initial cost of the GPU hardware was greater than the CPU (£4080 vs. £2876), but the median energy consumption per case was significantly less using the GPU method (8.44 (6.80-13.39) Wh vs. 2.60 (2.16-3.12) Wh, P < 0.0001).

Conclusion: This study demonstrates that vFFR can be computed using 3D-CFD with up to 28-fold acceleration than previous techniques with no clinically significant sacrifice in accuracy.

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