Skip to main content

Advertisement

Log in

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

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

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.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  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 27:1975–1982

    PubMed  CAS  Google Scholar 

  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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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 247:808–817

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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 7:2790–2799

    PubMed  PubMed Central  Google Scholar 

  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 28:761–766

    PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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 63:175–181

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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 27:859–867

    PubMed  CAS  Google Scholar 

  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 36:715–725

    Article  PubMed  CAS  Google Scholar 

  23. Rosen B, Belliveau J, Vevea J, Brady T (1990) Perfusion imaging with NMR contrast agents. Magn Reson Med 14:249–265

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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 40:383–396

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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 281:553–566

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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. 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yoshiyuki Watanabe.

Ethics declarations

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arisawa, A., Watanabe, Y., Tanaka, H. et al. Comparative study of pulsed-continuous arterial spin labeling and dynamic susceptibility contrast imaging by histogram analysis in evaluation of glial tumors. Neuroradiology 60, 599–608 (2018). https://doi.org/10.1007/s00234-018-2024-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-018-2024-2

Keywords

Navigation