Identifying perfusion deficits on CT perfusion images using temporal similarity perfusion (TSP) mapping

  • Jill B. De VisEmail author
  • Sunbin Song
  • Marie Luby
  • Jan Willem Dankbaar
  • Daniel Glen
  • Richard Reynolds
  • Brigitta K. Velthuis
  • Wouter Kroon
  • Lawrence L. Latour
  • Reinoud P. H. Bokkers
Computed Tomography



Deconvolution-derived maps of CT perfusion (CTP) data may be confounded by transit delays. We propose temporal similarity perfusion (TSP) analysis to decrease CTP maps’ dependence on transit times and investigate its sensitivity to detect perfusion deficits.


CTP data of acute stroke patients obtained within 9 h of symptom onset was analyzed using a delay-insensitive singular value decomposition method and with TSP. The TSP method applies an iterative process whereby a pixel’s highest Pearson’s R value is obtained through comparison of a pixel’s time-shifted signal density time-series curve and the average whole brain signal density time-series curve. Our evaluation included a qualitative and quantitative rating of deconvolution maps (MTT, CBV, and TTP), of TSP maps, and of follow-up CT.


Sixty-five patients (mean 68 (SD 13) years, 34 male) were included. A perfusion deficit was identified in 90%, 86%, 65%, and 84% of MTT, TTP, CBV, and TSP maps. The agreement of MTT, TTP, and TSP with CT follow-up was comparable but noticeably lower for CBV. CBV had the best relationship with final infarct volume (R2 = 0.77, p < 0.001), followed by TSP (R2 = 0.63, p < 0.001). Intra-rater agreement of an inexperienced reader was higher for TSP than for CBV/MTT maps (kappa’s of 0.79–0.84 and 0.63–0.7). Inter-rater agreement for experienced readers was comparable across maps.


TSP maps are easier to interpret for inexperienced readers. Perfusion deficits detected by TSP are smaller which may suggest less dependence on transit delays although more investigation is required.

Key Points

• Temporal similarity perfusion mapping assesses CTP data based on similarities in signal time-curves.

• TSP maps are comparable in perfusion deficit detection to deconvolution maps.

• TSP maps are easier to interpret for inexperienced readers.


Humans Stroke Brain ischemia Perfusion Tomography, X-ray computed 



Anterior cerebral artery


Alberta stroke program early CT score


Cerebral blood flow


Cerebral blood volume


Computed tomography


CT angiography


CT perfusion


DUtch acute STroke study




Intra-arterial therapy


Internal carotid artery


Intravenous recombinant tissue plasminogen activator


Middle cerebral artery


Mechanical thrombectomy


Mean transit time


Non-contrast CT


National Institutes of Health Stroke Scale


Posterior cerebral artery


Positron emission tomography


Temporal similarity perfusion




Vertebrobasilar artery



This study has received funding by the Netherlands Heart Foundation (grant numbers 2008T034, 2012T061, and 2013T047) and the Nuts Ohra Foundation (grant number 0903–012).

Compliance with ethical standards


The scientific guarantor of this publication is Reinoud P.H. Bokkers.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in studies related to the Dutch acute STroke study ( Unique identifier: NCT00880113).


• retrospective

• cross-sectional study

• multicenter study

Supplementary material

330_2018_5896_MOESM1_ESM.webloc (1 kb)
ESM 1 (WEBLOC 1 kb)


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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Jill B. De Vis
    • 1
    • 2
    Email author
  • Sunbin Song
    • 3
  • Marie Luby
    • 2
  • Jan Willem Dankbaar
    • 4
  • Daniel Glen
    • 3
  • Richard Reynolds
    • 3
  • Brigitta K. Velthuis
    • 4
  • Wouter Kroon
    • 1
  • Lawrence L. Latour
    • 2
  • Reinoud P. H. Bokkers
    • 1
  1. 1.Department of Radiology, Medical Imaging CenterUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
  2. 2.National Institute of Neurological Disorders and Stroke, Stroke BranchNational Institutes of HealthBethesdaUSA
  3. 3.National Institute of Mental Health, Scientific and Statistical Computing CoreNational Institutes of HealthBethesdaUSA
  4. 4.Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands

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