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Reliability of cerebral vein volume quantification based on susceptibility-weighted imaging

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Abstract

Introduction

Susceptibility-weighted imaging (SWI) visualizes even small cerebral veins and might, therefore, be valuable in monitoring neurological diseases affecting cerebral veins. Since it is generally difficult to evaluate individual results of quantitative MRI measurements, an automatic approach would be highly appreciated to assist the diagnostic process. The aim of this study was to evaluate the rescan and reanalysis reliability using an automatic venous volumetric approach based on SWI in healthy controls.

Methods

SWI was performed in ten healthy controls undergoing MRI examinations using a 32-channel head coil at 3 T five times on five different days. To test for rescan and reanalysis variability, the deep cerebral vein volume was quantified using ANTs and SPM8.

Results

Total volumes of cerebral deep veins measured during five MRI scans in ten individuals (n = 50 scans) showed intra-individual volume changes ranging from 0.07 to 1.03 ml (mean variability = 10.2 %). Automatic reanalyses revealed exactly the same results in all scans.

Conclusion

Automatic SWI-based cerebral vein volumetry shows acceptable rescan—and excellent reanalyses—reliability in healthy volunteers. Therefore, this approach might be beneficial in intra-individual follow-up studies of neurological diseases affecting the cerebral venous system.

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References

  1. Ogawa S, Lee TM, Kay AR et al (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 87(24):9868–9872

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Reichenbach JR, Barth M, Haacke EM et al (2000) High-resolution MR venography at 3.0 Tesla. J Comput Assist Tomogr 24(6):949–957

    Article  CAS  PubMed  Google Scholar 

  3. Haacke EM, Xu Y, Cheng YN et al (2004) Susceptibility weighted imaging (SWI). Magn Reson Med 52(3):612–618. doi:10.1002/mrm.20198

    Article  PubMed  Google Scholar 

  4. Reichenbach JR, Venkatesan R, Schillinger DJ et al (1997) Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology 204(1):272–277. doi:10.1148/radiology.204.1.9205259

    Article  CAS  PubMed  Google Scholar 

  5. Sehgal V, Delproposto Z, Haacke EM et al (2005) Clinical applications of neuroimaging with susceptibility-weighted imaging. J Magn Reson Imaging 22(4):439–450. doi:10.1002/jmri.20404

    Article  PubMed  Google Scholar 

  6. Thomas B, Somasundaram S, Thamburaj K et al (2008) Clinical applications of susceptibility weighted MR imaging of the brain—a pictorial review. Neuroradiology 50(2):105–116. doi:10.1007/s00234-007-0316-z

    Article  PubMed  Google Scholar 

  7. Tsui Y, Tsai FY, Hasso AN et al (2009) Susceptibility-weighted imaging for differential diagnosis of cerebral vascular pathology: a pictorial review. J Neurol Sci 287(1-2):7–16. doi:10.1016/j.jns.2009.08.064

    Article  PubMed  Google Scholar 

  8. Barnes SRS, Haacke EM (2009) Susceptibility-weighted imaging: clinical angiographic applications. Magn Reson Imaging Clin N Am 17(1):47–61. doi:10.1016/j.mric.2008.12.002

    Article  PubMed  PubMed Central  Google Scholar 

  9. Luo S, Yang L, Wang L (2014) Comparison of susceptibility-weighted and perfusion-weighted magnetic resonance imaging in the detection of penumbra in acute ischemic stroke. J Neuroradiol. doi:10.1016/j.neurad.2014.07.002

    Google Scholar 

  10. Hermier M, Nighoghossian N (2004) Contribution of susceptibility-weighted imaging to acute stroke assessment. Stroke 35(8):1989–1994. doi:10.1161/01.STR.0000133341.74387.96

    Article  PubMed  Google Scholar 

  11. Mahvash M, Pechlivanis I, Charalampaki P et al (2014) Visualization of small veins with susceptibility-weighted imaging for stereotactic trajectory planning in deep brain stimulation. Clin Neurol Neurosurg 124:151–155. doi:10.1016/j.clineuro.2014.06.041

    Article  PubMed  Google Scholar 

  12. Xia X, Tan C (2013) A quantitative study of magnetic susceptibility-weighted imaging of deep cerebral veins. J Neuroradiol 40(5):355–359. doi:10.1016/j.neurad.2013.03.005

    Article  PubMed  Google Scholar 

  13. Fushimi Y, Miki Y, Mori N et al (2010) Signal changes in the brain on susceptibility-weighted imaging under reduced cerebral blood flow: a preliminary study. J Neuroimaging 20(3):255–259. doi:10.1111/j.1552-6569.2008.00348.x

    Article  PubMed  Google Scholar 

  14. Sedlacik J, Helm K, Rauscher A et al (2008) Investigations on the effect of caffeine on cerebral venous vessel contrast by using susceptibility-weighted imaging (SWI) at 1.5, 3 and 7 T. Neuroimage 40(1):11–18. doi:10.1016/j.neuroimage.2007.11.046

    Article  PubMed  Google Scholar 

  15. Chang K, Barnes S, Haacke EM et al (2014) Imaging the effects of oxygen saturation changes in voluntary apnea and hyperventilation on susceptibility-weighted imaging. AJNR Am J Neuroradiol 35(6):1091–1095. doi:10.3174/ajnr.A3818

    Article  CAS  PubMed  Google Scholar 

  16. Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320. doi:10.1109/TMI.2010.2046908

    Article  PubMed  PubMed Central  Google Scholar 

  17. Avants BB, Tustison NJ, Song G et al (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044. doi:10.1016/j.neuroimage.2010.09.025

    Article  PubMed  Google Scholar 

  18. Bates D, Mächler M, Bolker B et al (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. doi:10.18637/jss.v067.i01

    Article  Google Scholar 

  19. Nowinski WL, Puspitasari F, Volkau I et al (2013) Quantification of the human cerebrovasculature: a 7 Tesla and 320-row CT in vivo study. J Comput Assist Tomogr 37(1):117–122. doi:10.1097/RCT.0b013e3182765906

    Article  PubMed  Google Scholar 

  20. Schaller B (2004) Physiology of cerebral venous blood flow: from experimental data in animals to normal function in humans. Brain Res Brain Res Rev 46(3):243–260. doi:10.1016/j.brainresrev.2004.04.005

    Article  CAS  PubMed  Google Scholar 

  21. Acosta-Cabronero J, Williams GB, Cardenas-Blanco A et al (2013) In vivo quantitative susceptibility mapping (QSM) in Alzheimer’s disease. PLoS ONE 8(11), e81093. doi:10.1371/journal.pone.0081093

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zheng W, Nichol H, Liu S et al (2013) Measuring iron in the brain using quantitative susceptibility mapping and X-ray fluorescence imaging. Neuroimage 78:68–74. doi:10.1016/j.neuroimage.2013.04.022

    Article  CAS  PubMed  Google Scholar 

  23. Einhäupl K, Stam J, Bousser M et al (2010) EFNS guideline on the treatment of cerebral venous and sinus thrombosis in adult patients. Eur J Neurol 17(10):1229–1235. doi:10.1111/j.1468-1331.2010.03011.x

    Article  PubMed  Google Scholar 

  24. Savoiardo M, Minati L, Farina L et al (2007) Spontaneous intracranial hypotension with deep brain swelling. Brain 130(Pt 7):1884–1893. doi:10.1093/brain/awm101

    Article  PubMed  Google Scholar 

  25. Mucke J, Möhlenbruch M, Kickingereder P et al (2015) Asymmetry of deep medullary veins on susceptibility weighted MRI in patients with acute MCA stroke is associated with poor outcome. PLoS ONE 10(4), e0120801. doi:10.1371/journal.pone.0120801

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Neuroradiology, University Medical Center Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany

    K. Egger, A. K. Dempfle, S. Yang, R. Schwarzwald & H. Urbach

  2. Department of Neurology, University Medical Center Freiburg, Freiburg, Germany

    A. Harloff

Authors
  1. K. Egger
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  2. A. K. Dempfle
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  3. S. Yang
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  4. R. Schwarzwald
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  5. A. Harloff
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  6. H. Urbach
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Corresponding author

Correspondence to K. Egger.

Ethics declarations

We declare that this human study has been approved by the local ethics committee and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. We declare that all patients gave informed consent prior to inclusion in this study.

Conflict of interest

We declare that we have no conflict of interest.

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Egger, K., Dempfle, A.K., Yang, S. et al. Reliability of cerebral vein volume quantification based on susceptibility-weighted imaging. Neuroradiology 58, 937–942 (2016). https://doi.org/10.1007/s00234-016-1712-z

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  • DOI: https://doi.org/10.1007/s00234-016-1712-z

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