Classifying Hemodynamics of MR Brain Perfusion Images Using Independent Component Analysis (ICA)

Yu Te Wu*, Yi Hsuan Kao, Wan Yuo Guo, Tzu Chen Yeh, Jen Chuen Hsieh, Michael Mu Huo Teng

*此作品的通信作者

研究成果: Paper同行評審

1 引文 斯高帕斯(Scopus)

摘要

Dynamic-susceptibility-contrast MR imaging is a widely used perfusion imaging technique that records signal changes on images caused by the passage of contrast-agent particles in the human brain after a bolus injection of contrast agent. The signal changes over time on different brain tissues represent distinct blood supply patterns and are crucial for studying cerebral hemodynamics. By assuming the spatial independence among these patterns, independent component analysis (ICA) was applied to classify different tissues, i.e., artery, gray matter, white matter, vein and sinus and choroid plexus, so that the spatio-temporal hemodynamics of these tissues were decomposed and analyzed. An arterial input function was modeled using the concentration-time curve of the arterial area for the deconvolution calculation of relative cerebral blood flow. The cerebral hemodynamic parameters, such as relative cerebral blood volume (CBV), relative cerebral blood flow (CBF), and relative mean transit time (MTT), were computed and their averaged ratios between gray matter and white matter were in good agreement with those in the literature.

原文English
頁面616-621
頁數6
DOIs
出版狀態Published - 7月 2003
事件International Joint Conference on Neural Networks 2003 - Portland, OR, United States
持續時間: 20 7月 200324 7月 2003

Conference

ConferenceInternational Joint Conference on Neural Networks 2003
國家/地區United States
城市Portland, OR
期間20/07/0324/07/03

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