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Supplementary dataset statement

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posted on 2025-01-09, 13:07 authored by Bao Li, hao sun, Yang Yang, Luyao Fan, Xueke Li, Jincheng Liu, Guangfei Li, Boyan Mao, Liyuan Zhang, Yanping Zhang, Jinping Dong, Jian Liu, Chang Hou, Lihua Wang, Honghui Zhang, Suqin Huang, Tengfei Li, Liyuan Kong, Zijie Wang, Huanmei Guo, Aike Qiao, Youjun Liu
The supplementary data of this study involves patient privacy, please forgive us for temporarily not being able to share these private data. We have uploaded a flow field example of one case, which was computed by computational fluid dynamics method. If the manuscript is accepted, we will apply to share all of these data (2255 cases). Our private dataset consists of some mechanical parameters in the fluid field, including 3D point cloud coordinates of geometric models, conservative volume fraction and the volume fraction gradient of simulated contrast in the fluid, flow velocity and the velocity gradient of the contrast, conservative volume fraction and the volume fraction gradient of the simulated blood in the fluid, flow velocity and the velocity gradient of the blood, pressure at each node, and volume of finite volumes. These data have great value for fluid mechanics and haemodynamics researchers if they want to conduct the deep learning study on fluid mechanics.

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