Analysis of Dynamic Susceptibility Contrast MRI Time Series Based on Unsupervised Clustering Methods
Meyer-Baese, A.; Lange, O.; Wismueller, A.; Hurdal, M.K.
Information Technology in Biomedicine, IEEE Transactions on
Volume 11, Issue 5, Sept. 2007 Page(s):563 - 573
Digital Object Identifier   10.1109/TITB.2007.897597
Summary:We compare five different unsupervised clustering techniques as tools for the analysis of dynamic susceptibility contrast MRI time series. The study included four subjects: two subjects with stroke and two subjects without focal neurological deficit. The goal was to determine the robustness and reliability of clustering methods in providing a self-organized segmentation of perfusion MRI data sharing common properties of signal dynamics. For this purpose, the relative signal reduction time series was computed for each pixel. Clustering of the resulting high-dimensional feature vectors was performed by minimal free-energy deterministic annealing, self-organizing maps, two variants of fuzzy c-means clustering (FVQ and FSM), and the neural gas algorithm. Clustering results were evaluated by visual assessment of cluster assignment maps and corresponding signal time curves as well as by quantitative comparison of cluster assignment maps with conventional pixel-specific perfusion parameter maps based on quantitative receiver operating characteristic (ROC) curve analysis. Clustering methods provided a functional segmentation with respect to vessel size, detected side asymmetries of contrast-agent first pass, and identified regions of perfusion deficits in subjects with stroke. As confirmed by quantitative ROC analysis, the clustering approach can detect regions of reduced brain perfusion with high accuracy when compared to conventional analysis by pixel-specific cerebral blood volume and mean transit time maps. We conclude that by unveiling differences of signal dynamics and amplitude, clustering is a useful tool to analyze and visualize regional properties of brain perfusion. Thus, it may contribute to the computer- aided diagnosis of cerebral circulation deficits by noninvasive neuroimaging.

» View citation and abstract

Log in by entering your IEEE Web Account Username and Password.

IEEE Communications Society members: If you subscribe to the IEEE Electronic Periodicals Package or IEEE Electronic Periodicals Package Plus, you must access your subscription at www.comsoc.org.

Check with your librarian, information professional, or system manager to determine if you need to log in. Please complete the online Technical Support Form if you need assistance.

Select the Purchase History link to access the document. You will have 5 Days after purchase to access the Full Text PDF. Please complete the online Technical Support Form if you need assistance.

• Search and access Abstract records free of charge
• Register for table of contents alerts
• Purchase Full Text PDF documents

» Learn more about subscription options or how to become an IEEE Member.