Advertisement

Neuroradiology

, Volume 60, Issue 6, pp 599–608 | Cite as

Comparative study of pulsed-continuous arterial spin labeling and dynamic susceptibility contrast imaging by histogram analysis in evaluation of glial tumors

  • Atsuko Arisawa
  • Yoshiyuki WatanabeEmail author
  • Hisashi Tanaka
  • Hiroto Takahashi
  • Chisato Matsuo
  • Takuya Fujiwara
  • Masahiro Fujiwara
  • Yasunori Fujimoto
  • Noriyuki Tomiyama
Diagnostic Neuroradiology

Abstract

Purpose

Arterial spin labeling (ASL) is a non-invasive perfusion technique that may be an alternative to dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for assessment of brain tumors. To our knowledge, there have been no reports on histogram analysis of ASL. The purpose of this study was to determine whether ASL is comparable with DSC-MRI in terms of differentiating high-grade and low-grade gliomas by evaluating the histogram analysis of cerebral blood flow (CBF) in the entire tumor.

Methods

Thirty-four patients with pathologically proven glioma underwent ASL and DSC-MRI. High-signal areas on contrast-enhanced T1-weighted images or high-intensity areas on fluid-attenuated inversion recovery images were designated as the volumes of interest (VOIs). ASL-CBF, DSC-CBF, and DSC-cerebral blood volume maps were constructed and co-registered to the VOI. Perfusion histogram analyses of the whole VOI and statistical analyses were performed to compare the ASL and DSC images.

Results

There was no significant difference in the mean values for any of the histogram metrics in both of the low-grade gliomas (n = 15) and the high-grade gliomas (n = 19). Strong correlations were seen in the 75th percentile, mean, median, and standard deviation values between the ASL and DSC images. The area under the curve values tended to be greater for the DSC images than for the ASL images.

Conclusions

DSC-MRI is superior to ASL for distinguishing high-grade from low-grade glioma. ASL could be an alternative evaluation method when DSC-MRI cannot be used, e.g., in patients with renal failure, those in whom repeated examination is required, and in children.

Keywords

Dynamic susceptibility contrast imaging Glial tumors Histogram analysis Pulsed-continuous arterial spin labeling 

Notes

Compliance with ethical standards

Funding

No funding was received for this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

For this type of study formal consent is not required.

References

  1. 1.
    Law M, Young R, Babb J, Rad M, Sasaki T, Zagzag D, Johnson G (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 27:1975–1982PubMedGoogle Scholar
  2. 2.
    Tietze A, Mouridsen K, Lassen-Ramshad Y, Ostergaard L (2015) Perfusion MRI derived indices of microvascular shunting and flow control correlate with tumor grade and outcome in patients with cerebral glioma. PLoS One 10:e0123044.  https://doi.org/10.1371/journal.pone.0123044 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S (2005) High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 60:493–502.  https://doi.org/10.1016/j.crad.2004.09.009 CrossRefPubMedGoogle Scholar
  4. 4.
    Wintermark M, Sesay M, Barbier E, Borbely K, Dillon WP, Eastwood JD, Glenn TC, Grandin CB, Pedraza S, Soustiel JF, Nariai T, Zaharchuk G, Caille JM, Dousset V, Yonas H (2005) Comparative overview of brain perfusion imaging techniques. Stroke 36:e83–e99.  https://doi.org/10.1161/01.STR.0000177884.72657.8b CrossRefPubMedGoogle Scholar
  5. 5.
    Emblem KE, Nedregaard B, Nome T, Due-Tonnessen P, Hald JK, Scheie D, Borota OC, Cvancarova M, Bjornerud A (2008) Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 247:808–817CrossRefPubMedGoogle Scholar
  6. 6.
    Young R, Babb J, Law M, Pollack E, Johnson G (2007) Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. J Magn Reson Imaging 26:1053–1063.  https://doi.org/10.1002/jmri.21064 CrossRefPubMedGoogle Scholar
  7. 7.
    Rau MK, Braun C, Skardelly M, Schittenhelm J, Paulsen F, Bender B, Ernemann U, Bisdas S (2014) Prognostic value of blood flow estimated by arterial spin labeling and dynamic susceptibility contrast-enhanced MR imaging in high-grade gliomas. J Neuro-Oncol 120:557–566.  https://doi.org/10.1007/s11060-014-1586-z CrossRefGoogle Scholar
  8. 8.
    Grade M, Hernandez Tamames JA, Pizzini FB, Achten E, Golay X, Smits M (2015) A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice. Neuroradiology 57:1181–1202.  https://doi.org/10.1007/s00234-015-1571-z CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    McDonald RJ, McDonald JS, Kallmes DF, Jentoft ME, Murray DL, Thielen KR, Williamson EE, Eckel LJ (2015) Intracranial gadolinium deposition after contrast-enhanced MR imaging. Radiology 275:772–782.  https://doi.org/10.1148/radiol.15150025 CrossRefPubMedGoogle Scholar
  10. 10.
    Kanda T, Fukusato T, Matsuda M, Toyoda K, Oba H, Kotoku J, Haruyama T, Kitajima K, Furui S (2015) Gadolinium-based contrast agent accumulates in the brain even in subjects without severe renal dysfunction: evaluation of autopsy brain specimens with inductively coupled plasma mass spectroscopy. Radiology 276:228–232.  https://doi.org/10.1148/radiol.2015142690 CrossRefPubMedGoogle Scholar
  11. 11.
    Murata N, Gonzalez-Cuyar LF, Murata K, Fligner C, Dills R, Hippe D, Maravilla KR (2016) Macrocyclic and other non-group 1 gadolinium contrast agents deposit low levels of gadolinium in brain and bone tissue: preliminary results from 9 patients with normal renal function. Investig Radiol 51:447–453.  https://doi.org/10.1097/RLI.0000000000000252 CrossRefGoogle Scholar
  12. 12.
    Weber MA, Gunther M, Lichy MP, Delorme S, Bongers A, Thilmann C, Essig M, Zuna I, Schad LR, Debus J, Schlemmer HP (2003) Comparison of arterial spin-labeling techniques and dynamic susceptibility-weighted contrast-enhanced MRI in perfusion imaging of normal brain tissue. Investig Radiol 38:712–718.  https://doi.org/10.1097/01.rli.0000084890.57197.54 CrossRefGoogle Scholar
  13. 13.
    Hirai T, Kitajima M, Nakamura H, Okuda T, Sasao A, Shigematsu Y, Utsunomiya D, Oda S, Uetani H, Morioka M, Yamashita Y (2011) Quantitative blood flow measurements in gliomas using arterial spin-labeling at 3T: intermodality agreement and inter- and intraobserver reproducibility study. AJNR Am J Neuroradiol 32:2073–2079.  https://doi.org/10.3174/ajnr.A2725 CrossRefPubMedGoogle Scholar
  14. 14.
    Lehmann P, Monet P, de Marco G, Saliou G, Perrin M, Stoquart-Elsankari S, Bruniau A, Vallee JN (2010) A comparative study of perfusion measurement in brain tumours at 3 tesla MR: arterial spin labeling versus dynamic susceptibility contrast-enhanced MRI. Eur Neurol 64:21–26.  https://doi.org/10.1159/000311520 CrossRefPubMedGoogle Scholar
  15. 15.
    Cebeci H, Aydin O, Ozturk-Isik E, Gumus C, Inecikli F, Bekar A, Kocaeli H, Hakyemez B (2014) Assesment of perfusion in glial tumors with arterial spin labeling; comparison with dynamic susceptibility contrast method. Eur J Radiol 83:1914–1919.  https://doi.org/10.1016/j.ejrad.2014.07.002 CrossRefPubMedGoogle Scholar
  16. 16.
    Jiang J, Zhao L, Zhang Y, Zhang S, Yao Y, Qin Y, Wang C, Zhu W (2014) Comparative analysis of arterial spin labeling and dynamic susceptibility contrast perfusion imaging for quantitative perfusion measurements of brain tumors. Int J Clin Exp Pathol 7:2790–2799PubMedPubMedCentralGoogle Scholar
  17. 17.
    Law M, Young R, Babb J, Pollack E, Johnson G (2007) Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 28:761–766PubMedGoogle Scholar
  18. 18.
    Abe T, Mizobuchi Y, Sako W, Irahara S, Otomi Y, Obama Y, Nakajima K, Khashbat D, Majigsuren M, Kageji T, Nagahiro S, Harada M (2015) Clinical significance of discrepancy between arterial spin labeling images and contrast-enhanced images in the diagnosis of brain tumors. Magn Reson Med Sci 14:313–319.  https://doi.org/10.2463/mrms.2014-0083 CrossRefPubMedGoogle Scholar
  19. 19.
    Khashbat D, Abe T, Ganbold M, Iwamoto S, Uyama N, Irahara S, Otomi Y, Harada M, Kageji T, Nagahiro S (2016) Correlation of 3D arterial spin labeling and multi-parametric dynamic susceptibility contrast perfusion MRI in brain tumors. J Med Investig 63:175–181CrossRefGoogle Scholar
  20. 20.
    Roy B, Awasthi R, Bindal A, Sahoo P, Kumar R, Behari S, Ojha B, Husain N, Pandey C, Rathore R, Gupta R (2013) Comparative evaluation of 3-dimensional pseudocontinuous arterial spin labeling with dynamic contrast-enhanced perfusion magnetic resonance imaging in grading of human glioma. J Comput Assist Tomogr 37:321–326.  https://doi.org/10.1097/RCT.0b013e318282d7e2 CrossRefPubMedGoogle Scholar
  21. 21.
    Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27:859–867PubMedGoogle Scholar
  22. 22.
    Ostergaard L, Weisskoff R, Chesler D, Gyldensted C, Rosen B (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36:715–725CrossRefPubMedGoogle Scholar
  23. 23.
    Rosen B, Belliveau J, Vevea J, Brady T (1990) Perfusion imaging with NMR contrast agents. Magn Reson Med 14:249–265CrossRefPubMedGoogle Scholar
  24. 24.
    Emblem KE, Due-Tonnessen P, Hald JK, Bjornerud A (2009) Automatic vessel removal in gliomas from dynamic susceptibility contrast imaging. Magn Reson Med 61:1210–1217.  https://doi.org/10.1002/mrm.21944 CrossRefPubMedGoogle Scholar
  25. 25.
    Arisawa A, Watanabe Y, Tanaka H, Takahashi H, Matsuo C, Fujiwara T, Fujimoto Y, Yamamoto K, Tomiyama N (2017) Vessel-masked perfusion magnetic resonance imaging with histogram analysis improves diagnostic accuracy for the grading of glioma. J Comput Assist Tomogr 41:910–915.  https://doi.org/10.1097/RCT.0000000000000614 CrossRefPubMedGoogle Scholar
  26. 26.
    Emblem KE, Bjornerud A (2009) An automatic procedure for normalization of cerebral blood volume maps in dynamic susceptibility contrast-based glioma imaging. AJNR Am J Neuroradiol 30:1929–1932.  https://doi.org/10.3174/ajnr.A1680 CrossRefPubMedGoogle Scholar
  27. 27.
    Buxton R, Frank L, Wong E, Siewert B, Warach S, Edelman R (1998) A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med 40:383–396CrossRefPubMedGoogle Scholar
  28. 28.
    Jung SC, Choi SH, Yeom JA, Kim JH, Ryoo I, Kim SC, Shin H, Lee AL, Yun TJ, Park CK, Sohn CH, Park SH (2013) Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods. PLoS One 8:e69323.  https://doi.org/10.1371/journal.pone.0069323 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Deibler AR, Pollock JM, Kraft RA, Tan H, Burdette JH, Maldjian JA (2008) Arterial spin-labeling in routine clinical practice, part 1: technique and artifacts. AJNR Am J Neuroradiol 29:1228–1234.  https://doi.org/10.3174/ajnr.A1030 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Teng MM, Cho IC, Kao YH, Chuang CS, Chiu FY, Chang FC (2013) Improvements in the quantitative assessment of cerebral blood volume and flow with the removal of vessel voxels from MR perfusion images. Biomed Res Int 2013:1–11.  https://doi.org/10.1155/2013/382027 CrossRefGoogle Scholar
  31. 31.
    Reishofer G, Koschutnig K, Enzinger C, Ischebeck A, Keeling S, Stollberger R, Ebner F (2011) Automated macrovessel artifact correction in dynamic susceptibility contrast magnetic resonance imaging using independent component analysis. Magn Reson Med 65:848–857.  https://doi.org/10.1002/mrm.22660 CrossRefPubMedGoogle Scholar
  32. 32.
    Deibler AR, Pollock JM, Kraft RA, Tan H, Burdette JH, Maldjian JA (2008) Arterial spin-labeling in routine clinical practice, part 3: hyperperfusion patterns. AJNR Am J Neuroradiol 29:1428–1435.  https://doi.org/10.3174/ajnr.A1034 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    van Westen D, Petersen ET, Wirestam R, Siemund R, Bloch KM, Stahlberg F, Bjorkman-Burtscher IM, Knutsson L (2011) Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours. MAGMA 24:211–223.  https://doi.org/10.1007/s10334-011-0255-x CrossRefPubMedGoogle Scholar
  34. 34.
    Wolf RL, Wang J, Wang S, Melhem ER, O'Rourke DM, Judy KD, Detre JA (2005) Grading of CNS neoplasms using continuous arterial spin labeled perfusion MR imaging at 3 Tesla. J Magn Reson Imaging 22:475–482.  https://doi.org/10.1002/jmri.20415 CrossRefPubMedGoogle Scholar
  35. 35.
    White CM, Pope WB, Zaw T, Qiao J, Naeini KM, Lai A, Nghiemphu PL, Wang JJ, Cloughesy TF, Ellingson BM (2014) Regional and voxel-wise comparisons of blood flow measurements between dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) and arterial spin labeling (ASL) in brain tumors. J Neuroimaging 24:23–30.  https://doi.org/10.1111/j.1552-6569.2012.00703.x CrossRefPubMedGoogle Scholar
  36. 36.
    Dangouloff-Ros V, Deroulers C, Foissac F, Badoual M, Shotar E, Grévent D, Calmon R, Pagès M, Grill J, Dufour C, Blauwblomme T, Puget S, Zerah M, Sainte-Rose C, Brunelle F, Varlet P, Boddaert N (2016) Arterial spin labeling to predict brain tumor grading in children: correlations between histopathologic vascular density and perfusion MR imaging. Radiology 281:553–566CrossRefPubMedGoogle Scholar
  37. 37.
    Yeom KW, Mitchell LA, Lober RM, Barnes PD, Vogel H, Fisher PG, Edwards MS (2014) Arterial spin-labeled perfusion of pediatric brain tumors. AJNR Am J Neuroradiol 35:395–401.  https://doi.org/10.3174/ajnr.A3670 CrossRefPubMedGoogle Scholar
  38. 38.
    Jarnum H, Steffensen EG, Knutsson L, Frund ET, Simonsen CW, Lundbye-Christensen S, Shankaranarayanan A, Alsop DC, Jensen FT, Larsson EM (2010) Perfusion MRI of brain tumours: a comparative study of pseudo-continuous arterial spin labelling and dynamic susceptibility contrast imaging. Neuroradiology 52:307–317.  https://doi.org/10.1007/s00234-009-0616-6 CrossRefPubMedGoogle Scholar
  39. 39.
    Kong L, Chen H, Yang Y, Chen L (2017) A meta-analysis of arterial spin labelling perfusion values for the prediction of glioma grade. Clin Radiol 72:255–261.  https://doi.org/10.1016/j.crad.2016.10.016 CrossRefPubMedGoogle Scholar
  40. 40.
    Noguchi T, Yoshiura T, Hiwatashi A, Togao O, Yamashita K, Nagao E, Shono T, Mizoguchi M, Nagata S, Sasaki T, Suzuki SO, Iwaki T, Kobayashi K, Mihara F, Honda H (2008) Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. AJNR Am J Neuroradiol 29:688–693.  https://doi.org/10.3174/ajnr.A0903 CrossRefPubMedGoogle Scholar
  41. 41.
    Ningning D, Haopeng P, Xuefei D, Wenna C, Yan R, Jingsong W, Chengjun Y, Zhenwei Y, Xiaoyuan F (2017) Perfusion imaging of brain gliomas using arterial spin labeling: correlation with histopathological vascular density in MRI-guided biopsies. Neuroradiology 59:51–59.  https://doi.org/10.1007/s00234-016-1756-0 CrossRefPubMedGoogle Scholar
  42. 42.
    Rogers TW, Toor G, Drummond K, Love C, Field K, Asher R, Tsui A, Buckland M, Gonzales M (2018) The 2016 revision of the WHO classification of central nervous system tumours: retrospective application to a cohort of diffuse gliomas. J Neuro-Oncol 137:181–189.  https://doi.org/10.1007/s11060-017-2710-7 CrossRefGoogle Scholar
  43. 43.
    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820.  https://doi.org/10.1007/s00401-016-1545-1 CrossRefPubMedGoogle Scholar
  44. 44.
    Lin Y, Xing Z, She D, Yang X, Zheng Y, Xiao Z, Wang X, Cao D (2017) IDH mutant and 1p/19q co-deleted oligodendrogliomas: tumor grade stratification using diffusion-, susceptibility-, and perfusion-weighted MRI. Neuroradiology 59:555–562.  https://doi.org/10.1007/s00234-017-1839-6 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D (2017) Noninvasive assessment of IDH mutational status in World Health Organization grade II and III astrocytomas using DWI and DSC-PWI combined with conventional MR imaging. AJNR Am J Neuroradiol 38:1138–1144.  https://doi.org/10.3174/ajnr.A5171 CrossRefPubMedGoogle Scholar
  46. 46.
    Brendle C, Hempel JM, Schittenhelm J, Skardelly M, Tabatabai G, Bender B, Ernemann U, Klose U (2017) Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol.  https://doi.org/10.1007/s00062-017-0590-z
  47. 47.
    Mikami T (2016) Diagnosis and pathophysiological analysis of moyamoya disease using MRI. Cereb Blood Flow Metab (Jpn J Cereb Blood Flow Metab) 27:307–312.  https://doi.org/10.16977/cbfm.27.2_307 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Atsuko Arisawa
    • 1
  • Yoshiyuki Watanabe
    • 1
    Email author
  • Hisashi Tanaka
    • 1
  • Hiroto Takahashi
    • 1
  • Chisato Matsuo
    • 1
  • Takuya Fujiwara
    • 1
  • Masahiro Fujiwara
    • 1
  • Yasunori Fujimoto
    • 2
  • Noriyuki Tomiyama
    • 1
  1. 1.Department of Diagnostic and Interventional RadiologyOsaka University Graduate School of MedicineOsakaJapan
  2. 2.Department of NeurosurgeryOsaka University Graduate School of MedicineOsakaJapan

Personalised recommendations