European Radiology

, Volume 26, Issue 11, pp 4204–4212 | Cite as

Optimising MR perfusion imaging: comparison of different software-based approaches in acute ischaemic stroke

  • Lars-Arne Schaafs
  • David Porter
  • Heinrich J. Audebert
  • Jochen B. Fiebach
  • Kersten Villringer
Neuro

Abstract

Objectives

Perfusion imaging (PI) is susceptible to confounding factors such as motion artefacts as well as delay and dispersion (D/D). We evaluate the influence of different post-processing algorithms on hypoperfusion assessment in PI analysis software packages to improve the clinical accuracy of stroke PI.

Methods

Fifty patients with acute ischaemic stroke underwent MRI imaging in the first 24 h after onset. Diverging approaches to motion and D/D correction were applied. The calculated MTT and CBF perfusion maps were assessed by volumetry of lesions and tested for agreement with a standard approach and with the final lesion volume (FLV) on day 6 in patients with persisting vessel occlusion.

Results

MTT map lesion volumes were significantly smaller throughout the software packages with correction of motion and D/D when compared to the commonly used approach with no correction (p = 0.001–0.022). Volumes on CBF maps did not differ significantly (p = 0.207–0.925). All packages with advanced post-processing algorithms showed a high level of agreement with FLV (ICC = 0.704–0.879).

Conclusions

Correction of D/D had a significant influence on estimated lesion volumes and leads to significantly smaller lesion volumes on MTT maps. This may improve patient selection.

Key Points

Assessment on hypoperfusion using advanced post-processing with correction for motion and D/D.

CBF appears to be more robust regarding differences in post-processing.

Tissue at risk is estimated more accurately by correcting software algorithms.

Advanced post-processing algorithms show a higher agreement with the final lesion volume.

Keywords

MRI Perfusion Stroke Software Image processing 

Abbreviations

AIF

arterial input function

CBF

cerebral blood flow

cSVD

singular value decomposition with a block-circulant matrix

D/D

delay and dispersion

FLV

final lesion volume

ICC

intraclass correlation coefficient

MTT

mean transit time

SVD

singular value decomposition

sSVD

standard truncated singular value decomposition

References

  1. 1.
    Davis SM, Donnan GA, Parsons MW et al (2008) Effects of alteplase beyond 3 h after stroke in the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET): a placebo-controlled randomised trial. Lancet Neurol 7:299–309CrossRefPubMedGoogle Scholar
  2. 2.
    Hacke W, Furlan AJ, Al-Rawi Y et al (2009) Intravenous desmoteplase in patients with acute ischaemic stroke selected by MRI perfusion-diffusion weighted imaging or perfusion CT (DIAS-2): a prospective, randomised, double-blind, placebo-controlled study. Lancet Neurol 8:141–150CrossRefPubMedGoogle Scholar
  3. 3.
    Hacke W, Albers G, Al-Rawi Y et al (2005) The Desmoteplase in Acute Ischemic Stroke Trial (DIAS): a phase II MRI-based 9-hour window acute stroke thrombolysis trial with intravenous desmoteplase. Stroke; J Cereb Circ 36:66–73CrossRefGoogle Scholar
  4. 4.
    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: Off J Soc Magn Reson Med 36:715–725CrossRefGoogle Scholar
  5. 5.
    Calamante F, Gadian DG, Connelly A (2000) Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med: Off J Soc Magn Reson Med 44:466–473CrossRefGoogle Scholar
  6. 6.
    Østergaard L (2005) Principles of cerebral perfusion imaging by bolus tracking. J Magn Reson Imaging 22:710–717CrossRefPubMedGoogle Scholar
  7. 7.
    Calamante F (2013) Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc 74:1–32CrossRefPubMedGoogle Scholar
  8. 8.
    Ebinger M, Brunecker P, Jungehulsing GJ et al (2010) Reliable perfusion maps in stroke MRI using arterial input functions derived from distal middle cerebral artery branches. Stroke; J Cereb Circ 41:95–101CrossRefGoogle Scholar
  9. 9.
    Calamante F, Thomas DL, Pell GS, Wiersma J, Turner R (1999) Measuring cerebral blood flow using magnetic resonance imaging techniques. J Cereb Blood Flow Metab: Off J Int Soc Cereb Blood Flow Metab 19:701–735CrossRefGoogle Scholar
  10. 10.
    Calamante F, Mørup M, Hansen LK (2004) Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med: Off J Soc Magn Reson Med 52:789–797CrossRefGoogle Scholar
  11. 11.
    Bleeker EJW, Webb AG, van Walderveen MAA, van Buchem MA, van Osch MJP (2012) Evaluation of signal formation in local arterial input function measurements of dynamic susceptibility contrast MRI. Magn Reson Med: Off J Soc Magn Reson Med 67:1324–1331CrossRefGoogle Scholar
  12. 12.
    Willats L, Christensen S, Ma HK, Donnan GA, Connelly A, Calamante F (2011) Validating a local Arterial Input Function method for improved perfusion quantification in stroke. J Cereb Blood Flow Metab: Off J Int Soc Cereb Blood Flow Metab 31:2189–2198CrossRefGoogle Scholar
  13. 13.
    Kosior RK, Kosior JC, Frayne R (2007) Improved dynamic susceptibility contrast (DSC)-MR perfusion estimates by motion correction. J Magn Reson Imaging 26:1167–1172CrossRefPubMedGoogle Scholar
  14. 14.
    Lorenz C, Benner T, Lopez CJ et al (2006) Effect of using local arterial input functions on cerebral blood flow estimation. J Magn Reson Imaging 24:57–65CrossRefPubMedGoogle Scholar
  15. 15.
    Brunecker P, Endres M, Nolte CH et al (2008) Evaluation of an AIF correction algorithm for dynamic susceptibility contrast-enhanced perfusion MRI. Magn Reson Med: Off J Soc Magn Reson Med 60:102–110CrossRefGoogle Scholar
  16. 16.
    Brunecker P, Villringer A, Schultze J et al (2007) Correcting saturation effects of the arterial input function in dynamic susceptibility contrast-enhanced MRI: a Monte Carlo simulation. Magn Reson Imaging 25:1300–1311CrossRefPubMedGoogle Scholar
  17. 17.
    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: Off J Soc Magn Reson Med 50:164–174CrossRefGoogle Scholar
  18. 18.
    Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156CrossRefPubMedGoogle Scholar
  19. 19.
    Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17:825–841CrossRefPubMedGoogle Scholar
  20. 20.
    Tourdias T, Renou P, Sibon I et al (2011) Final cerebral infarct volume is predictable by MR imaging at 1 week. AJNR Am J Neuroradiol 32:352–358CrossRefPubMedGoogle Scholar
  21. 21.
    Parsons MW, Yang Q, Barber PA et al (2001) Perfusion magnetic resonance imaging maps in hyperacute stroke: relative cerebral blood flow most accurately identifies tissue destined to infarct. Stroke; J Cereb Circ 32:1581–1587CrossRefGoogle Scholar
  22. 22.
    Zaro-Weber O, Moeller-Hartmann W, Heiss WD, Sobesky J (2010) MRI perfusion maps in acute stroke validated with 15O-water positron emission tomography. Stroke; J Cereb Circ 41:443–449CrossRefGoogle Scholar
  23. 23.
    van Osch MJ, Vonken EJ, Viergever MA, van der Grond J, Bakker CJ (2003) Measuring the arterial input function with gradient echo sequences. Magn Reson Med: Off J Soc Magn Reson Med 49:1067–1076CrossRefGoogle Scholar
  24. 24.
    Krings T, Reinges MH, Erberich S et al (2001) Functional MRI for presurgical planning: problems, artefacts, and solution strategies. J Neurol Neurosurg Psychiatry 70:749–760CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Oakes TR, Johnstone T, Ores Walsh KS et al (2005) Comparison of fMRI motion correction software tools. NeuroImage 28:529–543CrossRefPubMedGoogle Scholar
  26. 26.
    Sorensen AG, Copen WA, Ostergaard L et al (1999) Hyperacute stroke: simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time. Radiology 210:519–527CrossRefPubMedGoogle Scholar
  27. 27.
    Zaro-Weber O, Livne M, Martin SZ et al (2015) Comparison of the 2 Most Popular Deconvolution Techniques for the Detection of Penumbral Flow in Acute Stroke. Stroke; J Cereb Circ 46:2795–2799CrossRefGoogle Scholar
  28. 28.
    Meijs M, Christensen S, Lansberg MG, Albers GW, Calamante F (2015) Analysis of perfusion MRI in stroke: To deconvolve, or not to deconvolve. Magn Reson Med: Off J Soc Magn Reson Med. doi:10.1002/mrm.26024 Google Scholar
  29. 29.
    Galinovic I, Brunecker P, Ostwaldt AC, Soemmer C, Hotter B, Fiebach JB (2011) Fully automated postprocessing carries a risk of substantial overestimation of perfusion deficits in acute stroke magnetic resonance imaging. Cerebrovasc Dis 31:408–413, Basel, Switzerland CrossRefPubMedGoogle Scholar
  30. 30.
    Galinovic I, Ostwaldt A-C, Soemmer C et al (2011) Search for a map and threshold in perfusion MRI to accurately predict tissue fate: a protocol for assessing lesion growth in patients with persistent vessel occlusion. Cerebrovasc Dis 32:186–193, Basel, Switzerland CrossRefPubMedGoogle Scholar
  31. 31.
    Calamante F, Christensen S, Desmond PM, Østergaard L, Davis SM, Connelly A (2010) The physiological significance of the time-to-maximum (Tmax) parameter in perfusion MRI. Stroke; J Cereb Circ 41:1169–1174CrossRefGoogle Scholar
  32. 32.
    Kudo K, Sasaki M, Østergaard L et al (2011) Susceptibility of Tmax to tracer delay on perfusion analysis: quantitative evaluation of various deconvolution algorithms using digital phantoms. J Cereb Blood Flow Metab: Off J Int Soc Cereb Blood Flow Metab 31:908–912CrossRefGoogle Scholar
  33. 33.
    Brown TA, Luby M, Shah J, Giannakidis D, Latour LL (2015) Magnetic resonance imaging in acute ischemic stroke patients with mild symptoms: an opportunity to standardize intravenous thrombolysis. J Stroke Cerebrovasc Dis 24:1832–1840CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  1. 1.Department of RadiologyCharité-UniversitätsmedizinBerlinGermany
  2. 2.Academic Neuroradiology, Department of Neurology and Center for Stroke ResearchCharité-UniversitätsmedizinBerlinGermany
  3. 3.Fraunhofer Institute for Medical Image Computing MEVISBremenGermany
  4. 4.Department of Neurology with Experimental NeurologyCharité-UniversitätsmedizinBerlinGermany

Personalised recommendations