Journal of Digital Imaging

, Volume 18, Issue 3, pp 219–226 | Cite as

Functional Cluster Analysis of CT Perfusion Maps: A New Tool for Diagnosis of Acute Stroke?

  • Christian BaumgartnerEmail author
  • Kurt Gautsch
  • Christian Böhm
  • Stephan Felber

CT perfusion imaging constitutes an important contribution to the early diagnosis of acute stroke. Cerebral blood flow (CBF), cerebral blood volume (CBV) and time-to-peak (TTP) maps are used to estimate the severity of cerebral damage after acute ischemia. We introduce functional cluster analysis as a new tool to evaluate CT perfusion in order to identify normal brain, ischemic tissue and large vessels. CBF, CBV and TTP maps represent the basis for cluster analysis applying a partitioning (k-means) and density-based (density-based spatial clustering of applications with noise, DBSCAN) paradigm. In patients with transient ischemic attack and stroke, cluster analysis identified brain areas with distinct hemodynamic properties (gray and white matter) and segmented territorial ischemia. CBF, CBV and TTP values of each detected cluster were displayed. Our preliminary results indicate that functional cluster analysis of CT perfusion maps may become a helpful tool for the interpretation of perfusion maps and provide a rapid means for the segmentation of ischemic tissue.

Key words

Computed tomography perfusion imaging brain infarction cluster analysis 



We thank Mr. Mattias Bair for the software implementation. This study was supported by the Austrian Industrial Research Promotion Fund FFF (Grant no. HITT-10 UMIT).


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Copyright information

© SCAR (Society for Computer Applications in Radiology) 2005

Authors and Affiliations

  • Christian Baumgartner
    • 1
    Email author
  • Kurt Gautsch
    • 2
  • Christian Böhm
    • 3
  • Stephan Felber
    • 2
  1. 1.Research Group for Biomedical Data Mining, Institute for Information SystemsUniversity for Health Sciences, Medical Informatics and TechnologyHall in TirolAustria
  2. 2.Department for Radiology IIInnsbruck Medical UniversityInnsbruckAustria
  3. 3.Institute for Computer ScienceUniversity of MunichMunichGermany

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