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Perfusion measurement in brain gliomas using velocity-selective arterial spin labeling: comparison with pseudo-continuous arterial spin labeling and dynamic susceptibility contrast MRI

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the performance of velocity-selective (VS) ASL among patients with untreated gliomas by comparing with both pseudo-continuous (PC) ASL and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI).

Methods

Forty-four consecutive patients with newly diagnosed glioma who underwent preoperative perfusion MRI including VSASL, PCASL, and DSC-PWI between 2017 and 2019 were retrospectively evaluated. Visual inspection was performed to evaluate the tumor signal intensity relative to gray matter based on 1–5 score criteria and weighted kappa was used to evaluate the pair-wise concordance between VSASL or PCASL and DSC-PWI. The relative tumor blood flow (rTBF) was measured from sampling intra-tumoral areas of hot-spot on the blood flow map and normalized against the contralateral normal gray matter blood flow. Linear regression and Bland–Altman analyses were performed to evaluate the correlation and agreement of rTBF measurements between ASL methods and DSC-PWI. The ROC analysis was constructed to determine the diagnostic performance of three perfusion methods for grading gliomas.

Results

TBF maps derived from VSASL were more comparable with DSC-PWI than PCASL on visual inspection (weighted kappa of 0.90 vs 0.68). In quantitative analysis, VSASL-rTBF yielded higher correlation with the values from DSC-PWI than PCASL-rTBF (R2 = 80% vs 47%, p < 0.001 for both). Both ASL and DSC-derived rTBF showed good distinction between low-grade and high-grade gliomas (p < 0.001). Compared to PCASL, VSASL yielded superior diagnostic sensitivity, specificity, and accuracy in glioma grading.

Conclusions

VSASL showed great promise for accurate quantification of TBF and could potentially improve the diagnostic performance of ASL in preoperative grading of gliomas.

Key Points

• VSASL demonstrated a greater agreement with DSC-PWI than with PCASL on visual inspection and perfusion quantification.

• VSASL showed a higher diagnostic sensitivity, negative predictive value, and accuracy than PCASL for glioma grading.

• With the advantages of insensitivity to transit delay and no need of prescribing a labeling plane, VSASL could potentially improve the diagnostic performance of ASL for a more accurate, noninvasive quantification of TBF in patients with glioma.

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Abbreviations

ASL:

Arterial spin labeling

ATT:

Arterial transit time

CBF:

Cerebral blood flow

CBV:

Cerebral blood volume

DSC-PWI:

Dynamic susceptibility contrast-enhanced perfusion-weighted imaging

IDH:

Isocitrate dehydrogenase

MGMT:

O6-Methylguanine methyltransferase

PCASL:

Pseudo-continuous arterial spin labeling

PLD:

Post-labeling delay

ROC:

Receiver operating characteristic

ROI:

Region of interest

rTBF:

Relative tumor blood flow

TBF:

Tumor blood flow

VSASL:

Velocity-selective arterial spin labeling

WHO:

World Health Organization

References

  1. Ostrom QT, Gittleman H, Liao P et al (2017) CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro Oncol 19:V1–V88

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lapointe S, Perry A, Butowski NA (2018) Primary brain tumours in adults. Lancet 392:432–446

    Article  PubMed  Google Scholar 

  3. Folkman J (1971) Tumor angiogenesis: therapeutic implications. N Engl J Med 285:1182–1186

    Article  CAS  PubMed  Google Scholar 

  4. Jain RK, di Tomaso E, Duda DG, Loeffler JS, Sorensen AG, Batchelor TT (2007) Angiogenesis in brain tumours. Nat Rev Neurosci 8:610–622

    Article  CAS  PubMed  Google Scholar 

  5. Cha S, Tihan T, Crawford F et al (2005) Differentiation of low-grade oligodendrogliomas from low-grade astrocytomas by using quantitative blood-volume measurements derived from dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 26:266

    PubMed  PubMed Central  Google Scholar 

  6. Warmuth C, Gunther M, Zimmer C (2003) Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging. Radiology 228:523–532

    Article  PubMed  Google Scholar 

  7. Law M, Young RJ, Babb JS et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247:490–498

    Article  PubMed  Google Scholar 

  8. Paulson ES, Schmainda KM (2008) Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 249:601–613

    Article  PubMed  PubMed Central  Google Scholar 

  9. 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

    Article  CAS  PubMed  Google Scholar 

  10. Dai W, Garcia D, de Bazelaire C, Alsop DC (2008) Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields. Magn Reson Med 60:1488–1497

    Article  PubMed  PubMed Central  Google Scholar 

  11. Alsop DC, Detre JA, Golay X et al (2015) Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 73:102–116

    Article  PubMed  Google Scholar 

  12. Zaharchuk G, El Mogy IS, Fischbein NJ, Albers GW (2012) Comparison of arterial spin labeling and bolus perfusion-weighted imaging for detecting mismatch in acute stroke. Stroke 43:1843–1848

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Nael K, Meshksar A, Liebeskind DS, Coull BM, Krupinski EA, Villablanca JP (2013) Quantitative analysis of hypoperfusion in acute stroke: arterial spin labeling versus dynamic susceptibility contrast. Stroke 44:3090–3096

    Article  PubMed  PubMed Central  Google Scholar 

  14. Silva AC, Kim SG, Garwood M (2000) Imaging blood flow in brain tumors using arterial spin labeling. Magn Reson Med 44:169–173

    Article  CAS  PubMed  Google Scholar 

  15. Kimura H, Takeuchi H, Koshimoto Y et al (2006) Perfusion imaging of meningioma by using continuous arterial spin-labeling: comparison with dynamic susceptibility-weighted contrast-enhanced MR images and histopathologic features. AJNR Am J Neuroradiol 27:85–93

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Khashbat MdD, Abe MdT, Ganbold MdM et al (2016) Correlation of 3D arterial spin labeling and multi-parametric dynamic susceptibility contrast perfusion MRI in brain tumors. J Med Invest 63:175–181

    Article  Google Scholar 

  17. Wong EC, Cronin M, Wu WC, Inglis B, Frank LR, Liu TT (2006) Velocity-selective arterial spin labeling. Magn Reson Med 55:1334–1341

    Article  PubMed  Google Scholar 

  18. Qin Q, van Zijl PC (2016) Velocity-selective-inversion prepared arterial spin labeling. Magn Reson Med 76:1136–1148

    Article  PubMed  Google Scholar 

  19. van Osch MJ, Teeuwisse WM, van Walderveen MA, Hendrikse J, Kies DA, van Buchem MA (2009) Can arterial spin labeling detect white matter perfusion signal? Magn Reson Med 62:165–173

    Article  PubMed  Google Scholar 

  20. Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG (2003) Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 50:164–174

    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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. You S-H, Yun TJ, Choi HJ et al (2018) Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging. Eur Radiol 28:3801–3810

    Article  PubMed  Google Scholar 

  23. Sunwoo L, Yun T, You S et al (2016) Differentiation of glioblastoma from brain metastasis: qualitative and quantitative analysis using arterial spin labeling MR imaging. PLoS One 11:e0166662

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Dangouloff-Ros V, Deroulers C, Foissac F et al (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 

  25. Qiao XJ, Ellingson BM, Kim HJ et al (2015) Arterial spin-labeling perfusion MRI stratifies progression-free survival and correlates with epidermal growth factor receptor status in glioblastoma. AJNR Am J Neuroradiol 36:672–677

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Diedenhofen B, Musch J (2015) cocor: a comprehensive solution for the statistical comparison of correlations. PLoS One 10:e0121945

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Lev MH, Ozsunar Y, Henson JW et al (2004) Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 25:214–221

    PubMed  PubMed Central  Google Scholar 

  28. Alsop DC, Detre JA (1996) Reduced transit-time sensitivity in noninvasive magnetic resonance imaging of human cerebral blood flow. J Cereb Blood Flow Metab 16:1236–1249

    Article  CAS  PubMed  Google Scholar 

  29. Dai W, Robson PM, Shankaranarayanan A, Alsop DC (2012) Reduced resolution transit delay prescan for quantitative continuous arterial spin labeling perfusion imaging. Magn Reson Med 67:1252–1265

    Article  PubMed  Google Scholar 

  30. Qin Q, Huang AJ, Hua J, Desmond JE, Stevens RD, van Zijl PC (2014) Three-dimensional whole-brain perfusion quantification using pseudo-continuous arterial spin labeling MRI at multiple post-labeling delays: accounting for both arterial transit time and impulse response function. NMR Biomed 27:116–128

    Article  PubMed  Google Scholar 

  31. Buxton RB, Frank LR, Wong EC, Siewert B, Warach S, Edelman RR (1998) A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med 40:383–396

    Article  CAS  PubMed  Google Scholar 

  32. Wu WC, Jain V, Li C et al (2010) In vivo venous blood T1 measurement using inversion recovery true-FISP in children and adults. Magn Reson Med 64:1140–1147

    Article  PubMed  PubMed Central  Google Scholar 

  33. Qin Q, Strouse JJ, van Zijl PC (2011) Fast measurement of blood T(1) in the human jugular vein at 3 Tesla. Magn Reson Med 65:1297–1304

    Article  PubMed  Google Scholar 

  34. Varela M, Hajnal JV, Petersen ET, Golay X, Merchant N, Larkman DJ (2011) A method for rapid in vivo measurement of blood T1. NMR Biomed 24:80–88

    Article  PubMed  Google Scholar 

  35. Wang J, Alsop DC, Song HK et al (2003) Arterial transit time imaging with flow encoding arterial spin tagging (FEAST). Magn Reson Med 50:599–607

    Article  PubMed  Google Scholar 

  36. Qiu M, Paul Maguire R, Arora J et al (2010) Arterial transit time effects in pulsed arterial spin labeling CBF mapping: insight from a PET and MR study in normal human subjects. Magn Reson Med 63:374–384

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bokkers RP, Bremmer JP, van Berckel BN et al (2010) Arterial spin labeling perfusion MRI at multiple delay times: a correlative study with H(2)(15)O positron emission tomography in patients with symptomatic carotid artery occlusion. J Cereb Blood Flow Metab 30:222–229

    Article  PubMed  Google Scholar 

  38. MacIntosh BJ, Filippini N, Chappell MA, Woolrich MW, Mackay CE, Jezzard P (2010) Assessment of arterial arrival times derived from multiple inversion time pulsed arterial spin labeling MRI. Magn Reson Med 63:641–647

    Article  PubMed  Google Scholar 

  39. Duhamel G, de Bazelaire C, Alsop DC (2003) Evaluation of systematic quantification errors in velocity-selective arterial spin labeling of the brain. Magn Reson Med 50:145–153

    Article  PubMed  Google Scholar 

  40. Meakin JA, Jezzard P (2013) An optimized velocity selective arterial spin labeling module with reduced eddy current sensitivity for improved perfusion quantification. Magn Reson Med 69:832–838

    Article  PubMed  Google Scholar 

  41. Guo J, Meakin JA, Jezzard P, Wong EC (2015) An optimized design to reduce eddy current sensitivity in velocity-selective arterial spin labeling using symmetric BIR-8 pulses. Magn Reson Med 73:1085–1094

    Article  PubMed  Google Scholar 

  42. Qin Q, Shin T, Schär M, Guo H, Chen H, Qiao Y (2016) Velocity-selective magnetization-prepared non-contrast-enhanced cerebral MR angiography at 3 Tesla: improved immunity to B0/B1 inhomogeneity. Magn Reson Med 75:1232–1241

    Article  PubMed  Google Scholar 

  43. Li W, Xu F, Schär M et al (2018) Whole-brain arteriography and venography: using improved velocity-selective saturation pulse trains. Magn Reson Med 79:2014–2023

    Article  PubMed  Google Scholar 

  44. Shin T, Qin Q, Park J-Y, Crawford RS, Rajagopalan S (2016) Identification and reduction of image artifacts in non–contrast-enhanced velocity-selective peripheral angiography at 3T. Magn Reson Med 76:466–477

    Article  PubMed  Google Scholar 

  45. Shin T, Qin Q (2018) Characterization and suppression of stripe artifact in velocity-selective magnetization-prepared unenhanced MR angiography. Magn Reson Med 80:1997–2005

    Article  PubMed  PubMed Central  Google Scholar 

  46. Liu D, Xu F, Li W, van Zijl PC, Lin DD, Qin Q (2020) Improved velocity-selective-inversion arterial spin labeling for cerebral blood flow mapping with 3D acquisition. Magn Reson Med 84:2512–2522

    Article  PubMed  PubMed Central  Google Scholar 

  47. Franklin SL, Bones IK, Harteveld AA et al (2021) Multi-organ comparison of flow-based arterial spin labeling techniques: spatially non-selective labeling for cerebral and renal perfusion imaging. Magn Reson Med 85:2580–2594

    Article  PubMed  Google Scholar 

  48. Liu D, Li W, Xu F, Zhu D, Shin T, Qin Q (2021) Ensuring both velocity and spatial responses robust to field inhomogeneities for velocity-selective arterial spin labeling through dynamic phase-cycling. Magn Reson Med 85:2723–2734

    Article  PubMed  Google Scholar 

  49. Liu D, Xu F, Lin DD, van Zijl PC, Qin Q (2017) Quantitative measurement of cerebral blood volume using velocity-selective pulse trains. Magn Reson Med 77:92–101

    Article  PubMed  Google Scholar 

  50. Qin Q, Qu Y, Li W et al (2019) Cerebral blood volume mapping using Fourier-transform-based velocity-selective saturation pulse trains. Magn Reson Med 81:3544–3554

    Article  PubMed  PubMed Central  Google Scholar 

  51. Li W, Liu D, van Zijl PCM, Qin Q (2021) Three-dimensional whole-brain mapping of cerebral blood volume and venous cerebral blood volume using Fourier transform–based velocity-selective pulse trains. Magn Reson Med 86:1420–1433

    Article  PubMed  PubMed Central  Google Scholar 

  52. Choi YS, Ahn SS, Lee S-K et al (2017) Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume. Eur Radiol 27:3181–3189

    Article  PubMed  PubMed Central  Google Scholar 

  53. Shin JH, Lee HK, Kwun BD et al (2002) Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. AJR Am J Roentgenol 179:783–789

    Article  PubMed  Google Scholar 

  54. Cebeci H, Aydin O, Ozturk-Isik E et al (2014) Assesment of perfusion in glial tumors with arterial spin labeling; comparison with dynamic susceptibility contrast method. Eur J Radiol 83:1914–1919

    Article  CAS  PubMed  Google Scholar 

  55. Järnum H, Steffensen EG, Knutsson L et al (2010) Perfusion MRI of brain tumours: a comparative study of pseudo-continuous arterial spin labelling and dynamic susceptibility contrast imaging. Neuroradiology 52:307–317

    Article  PubMed  Google Scholar 

  56. Xu F, Li W, Liu P et al (2018) Accounting for the role of hematocrit in between-subject variations of MRI-derived baseline cerebral hemodynamic parameters and functional BOLD responses. Hum Brain Mapp 39:344–353

    Article  PubMed  Google Scholar 

  57. Zeng Q, Jiang B, Shi F, Ling C, Dong F, Zhang J (2017) 3D pseudocontinuous arterial spin-labeling MR imaging in the preoperative evaluation of gliomas. AJNR Am J Neuroradiol 38:1876–1883

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank the radiologist and nurse colleagues who helped during the research study. A special thank you is also expressed to the patients for participating in the study.

Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China

    Yaoming Qu, Dexia Kong, Haitao Wen, Xiaochan Ou, Qihong Rui, Xianlong Wang & Zhibo Wen

  2. The Russell H. Morgan, Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Doris D. Lin & Qin Qin

  3. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA

    Qin Qin

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  1. Yaoming Qu
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Correspondence to Zhibo Wen.

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Qu, Y., Kong, D., Wen, H. et al. Perfusion measurement in brain gliomas using velocity-selective arterial spin labeling: comparison with pseudo-continuous arterial spin labeling and dynamic susceptibility contrast MRI. Eur Radiol 32, 2976–2987 (2022). https://doi.org/10.1007/s00330-021-08406-7

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  • DOI: https://doi.org/10.1007/s00330-021-08406-7

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