Skip to main content

Advertisement

SpringerLink
Log in

Quantitative Rapid Assessment of Leukoaraiosis in CT

Comparison to Gold Standard MRI

  • Original Article
  • Published:
Clinical Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

The severity of white matter lesions (WML) is a risk factor of hemorrhage and predictor of clinical outcome after ischemic stroke; however, in contrast to magnetic resonance imaging (MRI) reliable quantification for this surrogate marker is limited for computed tomography (CT), the leading stroke imaging technique. We aimed to present and evaluate a CT-based automated rater-independent method for quantification of microangiopathic white matter changes.

Methods

Patients with suspected minor stroke (National Institutes of Health Stroke scale, NIHSS < 4) were screened for the analysis of non-contrast computerized tomography (NCCT) at admission and compared to follow-up MRI. The MRI-based WML volume and visual Fazekas scores were assessed as the gold standard reference. We employed a recently published probabilistic brain segmentation algorithm for CT images to determine the tissue-specific density of WM space. All voxel-wise densities were quantified in WM space and weighted according to partial probabilistic WM content. The resulting mean weighted density of WM space in NCCT, the surrogate of WML, was correlated with reference to MRI-based WML parameters.

Results

The process of CT-based tissue-specific segmentation was reliable in 79 cases with varying severity of microangiopathy. Voxel-wise weighted density within WM spaces showed a noticeable correlation (r = −0.65) with MRI-based WML volume. Particularly in patients with moderate or severe lesion load according to the visual Fazekas score the algorithm provided reliable prediction of MRI-based WML volume.

Conclusion

Automated observer-independent quantification of voxel-wise WM density in CT significantly correlates with microangiopathic WM disease in gold standard MRI. This rapid surrogate of white matter lesion load in CT may support objective WML assessment and therapeutic decision-making during acute stroke triage.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Basile AM, Pantoni L, Pracucci G, Asplund K, Chabriat H, Erkinjuntti T, Fazekas F, Ferro JM, Hennerici M, O’Brien J, Scheltens P, Visser MC, Wahlund LO, Waldemar G, Wallin A, Inzitari D; LADIS Study Group. Age, hypertension, and lacunar stroke are the major determinants of the severity of age-related white matter changes. The LADIS (Leukoaraiosis and Disability in the Elderly) Study. Cerebrovasc Dis. 2006;21(5–6):315–22.

    Article  PubMed  Google Scholar 

  2. Arsava EM, Rahman R, Rosand J, Lu J, Smith EE, Rost NS, Singhal AB, Lev MH, Furie KL, Koroshetz WJ, Sorensen AG, Ay H. Severity of leukoaraiosis correlates with clinical outcome after ischemic stroke. Neurology. 2009;72(16):1403–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ay H, Arsava EM, Rosand J, Furie KL, Singhal AB, Schaefer PW, Wu O, Gonzalez RG, Koroshetz WJ, Sorensen AG. Severity of leukoaraiosis and susceptibility to infarct growth in acute stroke. Stroke. 2008;39(5):1409–13.

    Article  PubMed  Google Scholar 

  4. Curtze S, Melkas S, Sibolt G, Haapaniemi E, Mustanoja S, Putaala J, Sairanen T, Tiainen M, Tatlisumak T, Strbian D. Cerebral computed tomography-graded white matter lesions are associated with worse outcome after thrombolysis in patients with stroke. Stroke. 2015;46(6):1554–60.

    Article  PubMed  Google Scholar 

  5. Curtze S, Haapaniemi E, Melkas S, Mustanoja S, Putaala J, Sairanen T, Sibolt G, Tiainen M, Tatlisumak T, Strbian D. White matter lesions double the risk of post-thrombolytic intracerebral hemorrhage. Stroke. 2015;46(8):2149–55.

    Article  PubMed  Google Scholar 

  6. Shi ZS, Loh Y, Liebeskind DS, Saver JL, Gonzalez NR, Tateshima S, Jahan R, Feng L, Vespa PM, Starkman S, Salamon N, Villablance JP, Ali LK, Ovbiagele B, Kim D, Viñuela F, Duckwiler GR. Leukoaraiosis predicts parenchymal hematoma after mechanical thrombectomy in acute ischemic stroke. Stroke. 2012;43(7):1806–11.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kuller LH, Longstreth WT Jr, Arnold AM, Bernick C, Bryan RN, Beauchamp NJ Jr; Cardiovascular Health Study Collaborative Research Group. White matter hyperintensity on cranial magnetic resonance imaging: a predictor of stroke. Stroke. 2004;35(8):1821–5.

    Article  PubMed  Google Scholar 

  8. Fazekas F, Barkhof F, Wahlund LO, Pantoni L, Erkinjuntti T, Scheltens P, Schmidt R. CT and MRI rating of white matter lesions. Cerebrovasc Dis. 2002;13 Suppl 2:31–6.

    Article  PubMed  Google Scholar 

  9. Maillard P, Delcroix N, Crivello F, Dufouil C, Gicquel S, Joliot M, Tzourio-Mazoyer N, Alpérovitch A, Tzourio C, Mazoyer B. An automated procedure for the assessment of white matter hyperintensities by multispectral (T1, T2, PD) MRI and an evaluation of its between-centre reproducibility based on two large community databases. Neuroradiology. 2008;50(1):31–42.

    Article  PubMed  Google Scholar 

  10. Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, Hoshi M, Ilg R, Schmid VJ, Zimmer C, Hemmer B, Mühlau M. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59(4):3774–83.

    Article  PubMed  Google Scholar 

  11. Masdeu JC, Irimia P, Asenbaum S, Bogousslavsky J, Brainin M, Chabriat H, Herholz K, Markus HS, Martínez-Vila E, Niederkorn K, Schellinger PD, Seitz RJ; EFNS. EFNS guideline on neuroimaging in acute stroke. Report of an EFNS task force. Eur J Neurol. 2006;13(12):1271–83.

    Article  CAS  PubMed  Google Scholar 

  12. Kemmling A, Wersching H, Berger K, Knecht S, Groden C, Nölte I. Decomposing the Hounsfield unit: probabilistic segmentation of brain tissue in computed tomography. Clin Neuroradiol. 2012;22(1):79–91.

    Article  CAS  PubMed  Google Scholar 

  13. Zhang J, Puri AS, Khan MA, Goddeau RP Jr, Henninger N. Leukoaraiosis predicts a poor 90-day outcome after Endovascular stroke therapy. AJNR Am J Neuroradiol. 2014;35(11):2070–5.

    Article  PubMed  Google Scholar 

  14. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol. 1987;149(2):351–6.

    Article  CAS  PubMed  Google Scholar 

  15. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37–46.

    Article  Google Scholar 

  16. Debette S, Beiser A, DeCarli C, Au R, Himali JJ, Kelly-Hayes M, Romero JR, Kase CS, Wolf PA, Seshadri S. Association of MRI markers of vascular brain injury with incident stroke, mild cognitive impairment, dementia, and mortality: the Framingham Offspring Study. Stroke. 2010;41(4):600–6.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bier G, Bongers MN, Ditt H, Bender B, Ernemann U, Horger M. Enhanced gray-white matter differentiation on non-enhanced CT using a frequency selective non-linear blending. Neuroradiology. 2016;58(7):649–55.

    Article  PubMed  Google Scholar 

  18. Salomon EJ, Barfett J, Willems PW, Geibprasert S, Bacigaluppi S, Krings T. Dynamic CT angiography and CT perfusion employing a 320-detector row CT: protocol and current clinical applications. Klin Neuroradiol. 2009;19(3):187–96.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Diagnostic and Interventional Neuroradiology, Universal Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Uta Hanning

  2. Department of Clinical Radiology, University Hospital of Münster, Münster, Germany

    Uta Hanning, Peter Bernhard Sporns & Thomas Niederstadt

  3. Department of Epidemiology and Social Medicine, University of Münster, Münster, Germany

    Uta Hanning

  4. Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany

    Rene Schmidt

  5. Department of Neurology, University Hospital of Münster, Münster, Germany

    Jens Minnerup

  6. Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University Tübingen, Tübingen, Germany

    Georg Bier

  7. Department of Neurology, Mauritius Hospital and University Hospital of Düsseldorf, Düsseldorf, Germany

    Stefan Knecht

  8. Institute of Neuroradiology, University Hospital of Lübeck, Lübeck, Germany

    André Kemmling

Authors
  1. Uta Hanning
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Bernhard Sporns
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rene Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Niederstadt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jens Minnerup
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Georg Bier
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stefan Knecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. André Kemmling
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uta Hanning.

Ethics declarations

Conflict of interests

U. Hanning, P. Sporns, R. Schmidt,T. Niederstadt, J. Minnerup,G. Bier, S. Knecht and A. Kemmling declare that they have no competing interests.

Additional information

Uta Hanning and Peter Sporns contributed equally to the manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hanning, U., Sporns, P.B., Schmidt, R. et al. Quantitative Rapid Assessment of Leukoaraiosis in CT. Clin Neuroradiol 29, 109–115 (2019). https://doi.org/10.1007/s00062-017-0636-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00062-017-0636-2

Keywords

Search

Navigation