Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm |
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Authors: | Seiichiro Morizawa Koji Shimoyama Shigeru Obayashi Kenichi Funamoto Toshiyuki Hayase |
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Institution: | (1) Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan |
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Abstract: | Abstract This study was performed to determine the relations between the features of wall shear stress and aneurysm rupture. For this
purpose, visual data mining was performed in unsteady blood flow simulation data for an aortic aneurysm. The time-series data
of wall shear stress given at each grid point were converted to spatial and temporal indices, and the grid points were sorted
using a self-organizing map based on the similarity of these indices. Next, the results of cluster analysis were mapped onto
the real space of the aortic aneurysm to specify the regions that may lead to aneurysm rupture. With reference to previous
reports regarding aneurysm rupture, the visual data mining suggested specific hemodynamic features that cause aneurysm rupture. |
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Keywords: | |
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