European Radiology

, Volume 27, Issue 6, pp 2381–2390 | Cite as

Increased susceptibility of asymmetrically prominent cortical veins correlates with misery perfusion in patients with occlusion of the middle cerebral artery

  • Yu Luo
  • Zhongying Gong
  • Yongming Zhou
  • Binge Chang
  • Chao Chai
  • Taiyuan Liu
  • Yanhong Han
  • Meiyun Wang
  • Tianyi Qian
  • E Mark Haacke
  • Shuang Xia
Magnetic Resonance

Abstract

Objectives

To evaluate tissue perfusion and venous susceptibility in ischaemic stroke patients as a means to predict clinical status and early prognosis.

Methods

A retrospective study of 51 ischaemic stroke patients were enrolled in this study. Susceptibility, perfusion and National Institute of Health stroke scale (NIHSS) were compared between patients with and without asymmetrically prominent cortical veins (APCVs). The correlation between susceptibility, perfusion and NIHSS was performed.

Results

Compared to patients without APCVs, the age of patients with APCVs was statistically older (p = 0.017). Patients with APCVs at discharge showed clinical deterioration in their NIHSS. Mean transit time (MTT), time to peak (TTP) and cerebral blood flow (CBF) in the stroke hemisphere were statistically delayed/decreased in patients with and without APCVs (all p < 0.05). In patients with APCVs, the changes in susceptibility positively correlated with increases in MTT and TTP (p < 0.05). Susceptibility and TTP positively correlated and CBF negatively correlated with NIHSS both at admission and discharge (p < 0.05).

Conclusions

Patients with APCVs have a tendency of deterioration. The presence of APCVs indicates the tissue has increased oxygen extraction fraction. Increased susceptibility from APCVs positively correlated with the delayed MTT and TTP, which reflects the clinical status at admission and predicts an early prognosis.

Key points

Patients with and without APCVs have similar misery perfusion.

Patients with APCVs have a tendency of deterioration compared to those without.

The presence of APCVs indicated the tissue has increased oxygen extraction fraction.

Increased susceptibility from APCVs positively correlated with the MTT and TTP.

Increased susceptibility from APCVs reflected the clinical status at admission.

Keywords

Middle cerebral artery Acute occlusion Misery perfusion Asymmetrically prominent cortical veins Susceptibility weighted imaging and mapping 

Abbreviations

APCVs

Asymmetrical prominent cortical veins

DWI

Diffusion-weighted imaging

MRA

Magnetic resonance angiography

NIHSS

National Institutes of Health Stroke Scale

OEF

Oxygen extraction fraction

PWI

Perfusion-weighted imaging

rCBF

Relative cerebral blood flow

rCBV

Relative cerebral blood volume

rMTT

Relative mean transit time

rSus

Relative susceptibility

rTTP

Relative time to peak

SWI

Susceptibility weighted imaging

SWIM

Susceptibility weighted imaging and mapping

VOI

Volume of interest

Notes

Acknowledgments

Supported by grants from the Natural Scientific Foundation of China (Grant No. 81501457 to Shuang Xia) and the Tianjin Health Bureau of Science and Technology (Grant No. 14KG103 to Shuang Xia); the National Natural Science Foundation of China (Grant No. 31470047, 81271565 to Meiyun Wang); the Henan Province Scientific and Technological Innovation Talents Project (Grant No. 164200510014 to MeiyunWang); the Shanghai Hongkou District Health Bureau Grand Science and Research Projects Foundation (Grant No.2012-01 to Yu Luo); the Shanghai Health Bureau Science and Research Projects Foundation (Grant No.2012-456 to Yu Luo).

The scientific guarantor of this publication is Shuang Xia and Meiyun Wang. The authors disclose no conflicts of interest. No complex statistical methods were necessary for this paper. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. No study subjects or cohorts have been previously reported. Methodology: retrospective, observable study. Performed at multiple centres.

References

  1. 1.
    Chen CY, Chen CI, Tsai FY, Tsai PH, Chan WP (2015) Prominent vessel sign on susceptibility-weighted imaging in acute stroke: prediction of infarct growth and clinical outcome. PLoS One 10:e0131118CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Polan RM, Poretti A, Huisman TA, Bosemani T (2015) Susceptibility-weighted imaging in pediatric arterial ischemic stroke: a valuable alternative for the noninvasive evaluation of altered cerebral hemodynamics. AJNR Am J Neuroradiol 36:783–788CrossRefPubMedGoogle Scholar
  3. 3.
    Park MG, Yang TI, Oh SJ, Baik SK, Kang YH, Park KP (2014) Multiple hypointense vessels on susceptibility-weighted imaging in acute ischemic stroke: surrogate marker of oxygen extraction fraction in penumbra? Cerebrovasc Dis 38:254–261CrossRefPubMedGoogle Scholar
  4. 4.
    Naik D, Viswamitra S, Kumar AA, Srinath MG (2014) Susceptibility weighted magnetic resonance imaging of brain: a multifaceted powerful sequence that adds to understanding of acute stroke. Ann Indian Acad Neurol 17:58–61CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Meoded A, Poretti A, Benson JE, Tekes A, Huisman TA (2014) Evaluation of the ischemic penumbra focusing on the venous drainage: the role of susceptibility weighted imaging (SWI) in pediatric ischemic cerebral stroke. J Neuroradiol 41:108–116CrossRefPubMedGoogle Scholar
  6. 6.
    Jensen-Kondering U, Bohm R (2013) Asymmetrically hypointense veins on T2*w imaging and susceptibility-weighted imaging in ischemic stroke. World J Radiol 5:156–165PubMedPubMedCentralGoogle Scholar
  7. 7.
    Xia S, Utriainen D, Tang J et al (2014) Decreased oxygen saturation in asymmetrically prominent cortical veins in patients with cerebral ischemic stroke. Magn Reson Imaging 32:1272–1276CrossRefPubMedGoogle Scholar
  8. 8.
    Hermier M, Nighoghossian N (2004) Contribution of susceptibility-weighted imaging to acute stroke assessment. Stroke 35:1989–1994CrossRefPubMedGoogle Scholar
  9. 9.
    Huang P, Chen CH, Lin WC, Lin RT, Khor GT, Liu CK (2012) Clinical applications of susceptibility weighted imaging in patients with major stroke. J Neurol 259:1426–1432CrossRefPubMedGoogle Scholar
  10. 10.
    Mittal S, Wu Z, Neelavalli J, Haacke EM (2009) Susceptibility-weighted imaging: technical aspects and clinical applications, part 2. AJNR Am J Neuroradiol 30:232–252CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Kao HW, Tsai FY, Hasso AN (2012) Predicting stroke evolution: comparison of susceptibility-weighted MR imaging with MR perfusion. Eur Radiol 22:1397–1403CrossRefPubMedGoogle Scholar
  12. 12.
    Viallon M, Altrichter S, Pereira VM et al (2010) Combined use of pulsed arterial spin-labeling and susceptibility-weighted imaging in stroke at 3T. Eur Neurol 64:286–296CrossRefPubMedGoogle Scholar
  13. 13.
    Verma RK, Hsieh K, Gratz PP et al (2014) Leptomeningeal collateralization in acute ischemic stroke: impact on prominent cortical veins in susceptibility-weighted imaging. Eur J Radiol 83:1448–1454CrossRefPubMedGoogle Scholar
  14. 14.
    Chai C, Yan S, Chu Z et al (2015) Quantitative measurement of brain iron deposition in patients with haemodialysis using susceptibility mapping. Metab Brain Dis 30:563–571CrossRefPubMedGoogle Scholar
  15. 15.
    Liu J, Xia S, Hanks R et al (2016) Susceptibility weighted imaging and mapping of micro-hemorrhages and major deep veins after traumatic brain injury. J Neurotrauma 33:10–21CrossRefPubMedGoogle Scholar
  16. 16.
    Liu C, Li W, Tong KA, Yeom KW, Kuzminski S (2015) Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 42:23–41CrossRefPubMedGoogle Scholar
  17. 17.
    Zaitsu Y, Kudo K, Terae S et al (2011) Mapping of cerebral oxygen extraction fraction changes with susceptibility-weighted phase imaging. Radiology 261:930–936CrossRefPubMedGoogle Scholar
  18. 18.
    Kou Z, Ye Y, Haacke EM (2015) Evaluating the role of reduced oxygen saturation and vascular damage in traumatic brain injury using magnetic resonance perfusion-weighted imaging and susceptibility-weighted imaging and mapping. Top Magn Reson Imaging 24:253–265CrossRefPubMedGoogle Scholar
  19. 19.
    Xia S, Zheng G, Shen W et al (2015) Quantitative measurements of brain iron deposition in cirrhotic patients using susceptibility mapping. Acta Radiol 56:339–346CrossRefPubMedGoogle Scholar
  20. 20.
    Wycliffe ND, Choe J, Holshouser B, Oyoyo UE, Haacke EM, Kido DK (2004) Reliability in detection of hemorrhage in acute stroke by a new three-dimensional gradient recalled echo susceptibility-weighted imaging technique compared to computed tomography: a retrospective study. J Magn Reson Imaging 20:372–377CrossRefPubMedGoogle Scholar
  21. 21.
    Special report from the National Institute of Neurological Disorders and Stroke (1990) Classification of cerebrovascular diseases III. Stroke 21:637–676CrossRefGoogle Scholar
  22. 22.
    Wang W, Zhang L, Liu W, Zhu Q, Lan Q, Zhao J (2016) Antiplatelet agents for the secondary prevention of ischemic stroke or transient ischemic attack: a network meta-analysis. J Stroke Cerebrovasc Dis 25:1081–1089CrossRefPubMedGoogle Scholar
  23. 23.
    Pandian DS, Ciulla C, Haacke EM, Jiang J, Ayaz M (2008) Complex threshold method for identifying pixels that contain predominantly noise in magnetic resonance images. J Magn Reson Imaging 28:727–735CrossRefPubMedGoogle Scholar
  24. 24.
    Haacke EM, Tang J, Neelavalli J, Cheng YC (2010) Susceptibility mapping as a means to visualize veins and quantify oxygen saturation. J Magn Reson Imaging 32:663–676CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Yu X, Yuan L, Jackson A et al (2016) Prominence of medullary veins on susceptibility-weighted images provides prognostic information in patients with subacute stroke. AJNR Am J Neuroradiol 37:423–429CrossRefPubMedGoogle Scholar
  26. 26.
    Astrup J, Siesjö BK, Symon L (1981) Thresholds in cerebral ischemia—the ischemic penumbra. Stroke 12:723–725CrossRefPubMedGoogle Scholar
  27. 27.
    Sakoh M, Ostergaard L, Gjedde A, Røhl L, Vestergaard-Poulsen P, Smith DF et al (2001) Prediction of tissue survival after middle cerebral artery occlusion based on changes in the apparent diffusion of water. J Neurosurg 95:450–458CrossRefPubMedGoogle Scholar
  28. 28.
    Sobesky J, Zaro Weber O, Lehnhardt FG, Hesselmann V, Thiel A, Dohmen C et al (2004) Which time-to-peak threshold best identifies penumbral flow? A comparison of perfusionweighted magnetic resonance imaging and positron emission tomography in acute ischemic stroke. Stroke 35:2843–2847CrossRefPubMedGoogle Scholar
  29. 29.
    Alves HC, Pacheco FT, Rocha AJ (2016) Collateral blood vessels in acute ischemic stroke: a physiological window to predict future outcomes. Arq Neuropsiquiatr 74:662–670CrossRefPubMedGoogle Scholar
  30. 30.
    Martín A, Macé E, Boisgard R, Montaldo G, Thézé B, Tanter M et al (2012) Imaging of perfusion, angiogenesis, and tissue elasticity after stroke. J Cereb Blood Flow Metab 32:1496–1507CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Luo S, Yang L, Wang L (2015) Comparison of susceptibility-weighted and perfusion-weighted magnetic resonance imaging in the detection of penumbra in acute ischemic stroke. J Neuroradiol 42:255–260CrossRefPubMedGoogle Scholar
  32. 32.
    Wang XC, Gao PY, Xue J, Liu GR, Ma L (2010) Identification of infarct core and penumbra in acute stroke using CT perfusion source images. AJNR Am J Neuroradiol 31:34–39CrossRefPubMedGoogle Scholar
  33. 33.
    Fisher M (2004) The ischemic penumbra: identification, evolution and treatment concepts. Cerebrovasc Dis 17(Suppl 1):1–6PubMedGoogle Scholar
  34. 34.
    Fujioka M, Okuchi K, Iwamura A, Taoka T, Siesjo BK (2013) A mismatch between the abnormalities in diffusion- and susceptibility-weighted magnetic resonance imaging may represent an acute ischemic penumbra with misery perfusion. J Stroke Cerebrovasc Dis 22:1428–1431CrossRefPubMedGoogle Scholar
  35. 35.
    Kim YW, Kim HJ, Choi SH, Kim DC (2014) Prominent hypointense veins on susceptibility weighted image in the cat brain with acute infarction: DWI, SWI, and PWI. Acta Radiol 55:1008–1014CrossRefPubMedGoogle Scholar
  36. 36.
    Hodel J, Rodallec M, Gerber S et al (2012) Susceptibility weighted magnetic resonance sequences “SWAN, SWI and VenoBOLD”: technical aspects and clinical applications. J Neuroradiol 39:71–86CrossRefPubMedGoogle Scholar
  37. 37.
    Roussel SA, van Bruggen N, King MD, Gadian DG (1995) Identification of collaterally perfused areas following focal cerebral ischemia in the rat by comparison of gradient echo and diffusion-weighted MRI. J Cereb Blood Flow Metab 15:578–586CrossRefPubMedGoogle Scholar
  38. 38.
    Toyama H, Takeshita G, Takeuchi A et al (1990) Cerebral hemodynamics in patients with chronic obstructive carotid disease by rCBF, rCBV, and rCBV/rCBF ratio using SPECT. J Nucl Med 31:55–60PubMedGoogle Scholar
  39. 39.
    Auer LM, Pucher R, Leber K, Ishiyama N (1987) Autoregulatory response of pial vessels in the cat. Neurol Res 9:245–248CrossRefPubMedGoogle Scholar
  40. 40.
    Bosemani T, Poretti A, Orman G, Meoded A, Huisman TA (2013) Pediatric cerebral stroke: susceptibility-weighted imaging may predict post-ischemic malignant edema. Neuroradiol J 26:579–583CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Baik SK, Choi W, Oh SJ et al (2012) Change in cortical vessel signs on susceptibility-weighted images after full recanalization in hyperacute ischemic stroke. Cerebrovasc Dis 34:206–212CrossRefPubMedGoogle Scholar
  42. 42.
    Parsons MW, Pepper EM, Bateman GA, Wang Y, Levi CR (2007) Identification of the penumbra and infarct core on hyperacute noncontrast and perfusion CT. Neurology 68:730–736CrossRefPubMedGoogle Scholar
  43. 43.
    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 37:1771–1777CrossRefPubMedGoogle Scholar
  44. 44.
    Kesavadas C, Thomas B, Pendharakar H, Sylaja PN (2011) Susceptibility weighted imaging: does it give information similar to perfusion weighted imaging in acute stroke? J Neurol 258:932–934CrossRefPubMedGoogle Scholar
  45. 45.
    Ueda T, Yuh WT, Maley JE, Quets JP, Hahn PY, Magnotta VA (1999) Outcome of acute ischemic lesions evaluated by diffusion and perfusion MR imaging. AJNR Am J Neuroradiol 20:983–989PubMedGoogle Scholar
  46. 46.
    Yata K, Suzuki A, Hatazawa J et al (2006) Relationship between cerebral circulatory reserve and oxygen extraction fraction in patients with major cerebral artery occlusive disease: a positron emission tomography study. Stroke 37:534–536CrossRefPubMedGoogle Scholar
  47. 47.
    Xia XB, Tan CL (2013) A quantitative study of magnetic susceptibility-weighted imaging of deep cerebral veins. J Neuroradiol 40:355–359CrossRefPubMedGoogle Scholar
  48. 48.
    Drier A, Tourdias T, Attal Y et al (2012) Prediction of subacute infarct size in acute middle cerebral artery stroke: comparison of perfusion-weighted imaging and apparent diffusion coefficient maps. Radiology 265:511–517CrossRefPubMedGoogle Scholar
  49. 49.
    Bang OY, Kim GM, Chung CS et al (2010) Differential pathophysiological mechanisms of stroke evolution between new lesions and lesion growth: perfusion-weighted imaging study. Cerebrovasc Dis 29:328–335CrossRefPubMedGoogle Scholar
  50. 50.
    Karonen JO, Liu Y, Vanninen RL et al (2000) Combined perfusion- and diffusion-weighted MR imaging in acute ischemic stroke during the 1st week: a longitudinal study. Radiology 217:886–894CrossRefPubMedGoogle Scholar
  51. 51.
    Wittsack HJ, Ritzl A, Fink GR et al (2002) MR imaging in acute stroke: diffusion-weighted and perfusion imaging parameters for predicting infarct size. Radiology 222:397–403CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Yu Luo
    • 1
  • Zhongying Gong
    • 2
  • Yongming Zhou
    • 1
  • Binge Chang
    • 3
  • Chao Chai
    • 4
  • Taiyuan Liu
    • 5
  • Yanhong Han
    • 5
  • Meiyun Wang
    • 5
  • Tianyi Qian
    • 6
  • E Mark Haacke
    • 7
  • Shuang Xia
    • 4
  1. 1.Radiology DepartmentBranch of Shanghai First Hospital No.1878ShanghaiChina
  2. 2.Neurological DepartmentTianjin First Central HospitalTianjinChina
  3. 3.Neurosurgery DepartmentTianjin First Central HospitalTianjinChina
  4. 4.Radiology DepartmentTianjin First Central HospitalTianjinChina
  5. 5.Radiology DepartmentZhengzhou University People’s HospitalZhengzhouChina
  6. 6.Siemens HealthcareMR collaborationBeijingChina
  7. 7.Radiology DepartmentWayne State UniversityDetroitUSA

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