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Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models



Lesion detection in acute stroke by computed-tomography perfusion (CTP) can be affected by incomplete bolus coverage in veins and hypoperfused tissue, so-called bolus truncation (BT), and low contrast-to-noise ratio (CNR). We examined the BT-frequency and hypothesized that image down-sampling and a vascular model (VM) for perfusion calculation would improve normo- and hypoperfused tissue classification.


CTP datasets from 40 acute stroke patients were retrospectively analysed for BT. In 16 patients with hypoperfused tissue but no BT, repeated 2-by-2 image down-sampling and uniform filtering was performed, comparing CNR to perfusion-MRI levels and tissue classification to that of unprocessed data. By simulating reduced scan duration, the minimum scan-duration at which estimated lesion volumes came within 10 % of their true volume was compared for VM and state-of-the-art algorithms.


BT in veins and hypoperfused tissue was observed in 9/40 (22.5 %) and 17/40 patients (42.5 %), respectively. Down-sampling to 128 × 128 resolution yielded CNR comparable to MR data and improved tissue classification (p = 0.0069). VM reduced minimum scan duration, providing reliable maps of cerebral blood flow and mean transit time: 5 s (p = 0.03) and 7 s (p < 0.0001), respectively).


BT is not uncommon in stroke CTP with 40-s scan duration. Applying image down-sampling and VM improve tissue classification.

Key points:

Too-short imaging duration is common in clinical acute stroke CTP imaging.

The consequence is impaired identification of hypoperfused tissue in acute stroke patients.

The vascular model is less sensitive than current algorithms to imaging duration.

Noise reduction by image down-sampling improves identification of hypoperfused tissue by CTP.

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Fig. 1
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Vascular model


Standard singular value decomposition


Circular singular-value decomposition with oscillation index


Contrast-to-noise ratio


Concentration-time curve


Arterial input function


Proportion of lesion detected






  1. Axel L (1983) Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique. Invest Radiol 18:94–99

    CAS  PubMed  Article  Google Scholar 

  2. Konstas AA, Goldmakher GV, Lee TY, Lev MH (2009) Theoretic basis and technical implementations of CT perfusion in acute ischemic stroke, part 2: technical implementations. AJNR Am J Neuroradiol 30:885–892

    CAS  PubMed  Article  Google Scholar 

  3. Wintermark M, Flanders AE, Velthuis B et al (2006) Perfusion-CT assessment of infarct core and penumbra: receiver operating characteristic curve analysis in 130 patients suspected of acute hemispheric stroke. Stroke 37:979–985

    PubMed  Article  Google Scholar 

  4. Campbell BC, Christensen S, Levi CR et al (2011) Cerebral blood flow is the optimal CT perfusion parameter for assessing infarct core. Stroke 42:3435–3440

    PubMed  Article  Google Scholar 

  5. Schaefer PW, Roccatagliata L, Ledezma C et al (2006) First-pass quantitative CT perfusion identifies thresholds for salvageable penumbra in acute stroke patients treated with intra-arterial therapy. AJNR Am J Neuroradiol 27:20–25

    CAS  PubMed  Google Scholar 

  6. Murphy BD, Fox AJ, Lee DH et al (2006) Identification of penumbra and infarct in acute ischemic stroke using computed tomography perfusion-derived blood flow and blood volume measurements. Stroke 37:1771–1777

    CAS  PubMed  Article  Google Scholar 

  7. Kudo K, Sasaki M, Yamada K et al (2010) Differences in CT perfusion maps generated by different commercial software: quantitative analysis by using identical source data of acute stroke patients. Radiology 254:200–209

    PubMed  Article  Google Scholar 

  8. Kudo K, Christensen S, Sasaki M et al (2013) Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom. Radiology 267:201–211

    PubMed Central  PubMed  Article  Google Scholar 

  9. Copen WA, Schaefer PW, Wu O (2011) MR perfusion imaging in acute ischemic stroke. Neuroimaging Clin N Am 21:259–283, x

    PubMed Central  PubMed  Article  Google Scholar 

  10. Deipolyi AR, Wu O, Macklin EA et al (2012) Reliability of cerebral blood volume maps as a substitute for diffusion-weighted imaging in acute ischemic stroke. J Magn Reson Imaging 36:1083–1087

    PubMed Central  PubMed  Article  Google Scholar 

  11. Wintermark M, Smith WS, Ko NU, Quist M, Schnyder P, Dillon WP (2004) Dynamic perfusion CT: optimizing the temporal resolution and contrast volume for calculation of perfusion CT parameters in stroke patients. AJNR Am J Neuroradiol 25:720–729

    PubMed  Google Scholar 

  12. Wintermark M, Albers GW, Alexandrov AV et al (2008) Acute stroke imaging research roadmap. AJNR Am J Neuroradiol 29:e23–e30

    PubMed Central  PubMed  Article  Google Scholar 

  13. Hirata M, Sugawara Y, Murase K, Miki H, Mochizuki T (2005) Evaluation of optimal scan duration and end time in cerebral CT perfusion study. Radiat Med 23:351–363

    PubMed  Google Scholar 

  14. Mouridsen K, Friston K, Hjort N, Gyldensted L, Ostergaard L, Kiebel S (2006) Bayesian estimation of cerebral perfusion using a physiological model of microvasculature. Neuroimage 33:570–579

    PubMed  Article  Google Scholar 

  15. Ibaraki M, Shimosegawa E, Toyoshima H, Takahashi K, Miura S, Kanno I (2005) Tracer delay correction of cerebral blood flow with dynamic susceptibility contrast-enhanced MRI. J Cereb Blood Flow Metab 25:378–390

    PubMed  Article  Google Scholar 

  16. Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG (2003) Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 50:164–174

    PubMed  Article  Google Scholar 

  17. Wittsack HJ, Wohlschlager AM, Ritzl EK et al (2008) CT-perfusion imaging of the human brain: advanced deconvolution analysis using circulant singular value decomposition. Comput Med Imaging Graph 32:67–77

    CAS  PubMed  Article  Google Scholar 

  18. Christensen S, Mouridsen K, Wu O et al (2009) Comparison of 10 perfusion MRI parameters in 97 sub-6-hour stroke patients using voxel-based receiver operating characteristics analysis. Stroke 40:2055–2061

    PubMed  Article  Google Scholar 

  19. Butcher KS, Parsons M, MacGregor L et al (2005) Refining the perfusion-diffusion mismatch hypothesis. Stroke 36:1153–1159

    CAS  PubMed  Article  Google Scholar 

  20. Uwano I, Kudo K, Sasaki M et al (2012) CT and MR perfusion can discriminate severe cerebral hypoperfusion from perfusion absence: evaluation of different commercial software packages by using digital phantoms. Neuroradiology 54:467–474

    PubMed  Article  Google Scholar 

  21. Fieselmann A, Kowarschik M, Ganguly A, Hornegger J, Fahrig R (2011) Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details. Int J Biomed Imaging 2011:467563

    PubMed Central  PubMed  Article  Google Scholar 

  22. Kamalian S, Kamalian S, Konstas AA et al (2012) CT perfusion mean transit time maps optimally distinguish benign oligemia from true "at-risk" ischemic penumbra, but thresholds vary by postprocessing technique. AJNR Am J Neuroradiol 33:545–549

    PubMed Central  PubMed  Article  Google Scholar 

  23. Silvennoinen HM, Hamberg LM, Valanne L, Hunter GJ (2007) Increasing Contrast Agent Concentration Improves Enhancement in First-Pass CT Perfusion. AJNR Am J Neuroradiol 28:1299–1303

    CAS  PubMed  Article  Google Scholar 

  24. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis. Magn Reson Med 36:715–725

    CAS  PubMed  Article  Google Scholar 

  25. Andersen IK, Szymkowiak A, Rasmussen CE et al (2002) Perfusion quantification using Gaussian process deconvolution. Magn Reson Med 48:351–361

    CAS  PubMed  Article  Google Scholar 

  26. Sylaja PN, Coutts SB, Subramaniam S et al (2007) Acute ischemic lesions of varying ages predict risk of ischemic events in stroke/TIA patients. Neurology 68:415–419

    CAS  PubMed  Article  Google Scholar 

  27. Kang DW, Latour LL, Chalela JA, Dambrosia JA, Warach S (2004) Early and late recurrence of ischemic lesion on MRI: evidence for a prolonged stroke-prone state? Neurology 63:2261–2265

    CAS  PubMed  Article  Google Scholar 

  28. Wahlgren NG, Ahmed N, Davalos A et al (2007) Thrombolysis with alteplase for acute ischaemic stroke in the Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST): an observational study. Lancet 369:275–282

    CAS  PubMed  Article  Google Scholar 

  29. Rothwell PM, Giles MF, Chandratheva A et al (2007) Effect of urgent treatment of transient ischaemic attack and minor stroke on early recurrent stroke (EXPRESS study): a prospective population-based sequential comparison. Lancet 370:1432–1442

    PubMed  Article  Google Scholar 

  30. Mikkelsen IK, Ribe LR, Bekke SL, Mouridsen K, Østergaard L (2013) The Robustness of DSC-PWI for Acute Stroke Imaging; Timing is Everything: The Vanishing Perfusion AbnormalityProc Int Soc Magn Reson Med p 0204, Salt Lake City, pp 0204

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The scientific guarantor of this publication is Professor Leif Østergaard. 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. This study has received funding from the Danish Agency for Science, Technology and Innovation, grant 09-065250. One of the authors (Kim Mouridsen) has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Thirty-two of the included subjects were previously investigated as part of an unrelated study investigating the fate of non-core-non-penumbral tissue. Lesion volumes on follow-up images for these patients were hence reported in Brain. 2011 Jun;134(Pt 6):1765-76. Methodology: retrospective, diagnostic or prognostic multi-centre study.

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Correspondence to Irene Klærke Mikkelsen.

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Mikkelsen, I.K., Jones, P.S., Ribe, L.R. et al. Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models. Eur Radiol 25, 2080–2088 (2015).

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  • CTP
  • Bolus truncation
  • Deconvolution
  • Acute stroke
  • Noise reduction