Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging

Fan Zhu, David Rodriguez Gonzalez, Trevor Carpenter, Malcolm Atkinson, Joanna Wardlaw

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Computer tomography (CT) perfusion imaging is widely used to calculate brain hemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that aid the diagnosis of acute stroke. Since perfusion source images contain more information than hemodynamic maps, good utilisation of the source images can lead to better understanding than the hemodynamic maps alone. Correlation-coefficient tests are used in our approach to measure the similarity between healthy tissue time-concentration curves and unknown curves. This information is then used to differentiate penumbra and dead tissues from healthy tissues. The goal of the segmentation is to fully utilize information in the perfusion source images. Our method directly identifies suspected abnormal areas from perfusion source images and then delivers a suggested segmentation of healthy, penumbra and dead tissue. This approach is designed to handle CT perfusion images, but it can also be used to detect lesion areas in MR perfusion images.
Original languageEnglish
Pages (from-to)950-958
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Early online date4 Mar 2013
Publication statusPublished - Sept 2013

Keywords / Materials (for Non-textual outputs)

  • CT
  • Perfusion Source Images
  • Pattern Recognition
  • Segmentation


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