Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm

研究成果: Review article査読

3 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)393-398
ページ数6
ジャーナルJournal of Visualization
14
4
DOI
出版ステータスPublished - 2011 12

ASJC Scopus subject areas

  • 凝縮系物理学
  • 電子工学および電気工学

フィンガープリント

「Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル