Skip to main content
SpringerLink
Log in

Cerebral hemodynamics in symptomatic anterior circulation intracranial stenosis measured by angiography-based quantitative flow ratio: association with CT perfusion

  • Vascular-Interventional
  • Published:
European Radiology Aims and scope Submit manuscript

A Commentary to this article was published on 21 April 2023

Abstract

Objectives

Cerebral hemodynamics is important for the management of intracranial atherosclerotic stenosis (ICAS). This study aimed to determine the utility of angiography-based quantitative flow ratio (QFR) to reflect cerebral hemodynamics in symptomatic anterior circulation ICAS by evaluating its association with CT perfusion (CTP).

Methods

Sixty-two patients with unilateral symptomatic stenosis in the intracranial internal carotid artery or middle cerebral artery who received percutaneous transluminal angioplasty (PTA) or PTA with stenting were included. Murray law–based QFR (μQFR) was computed from a single angiographic view. CTP parameters including cerebral blood flow, cerebral blood volume, mean transit time (MTT), and time to peak (TTP) were calculated, and relative values were obtained as the ratio between symptomatic and contralateral hemispheres. Relationships between μQFR and perfusion parameters, and between μQFR and perfusion response after intervention, were analyzed.

Results

Thirty-eight patients had improved perfusion after treatment. μQFR was significantly correlated with relative values of TTP and MTT, with correlation coefficients of  −0.45 and  −0.26, respectively, on a per-patient basis, and  −0.72 and  −0.43, respectively, on a per-vessel basis (all p < 0.05). Sensitivity and specificity for μQFR to diagnose hypoperfusion at a cut-off value of 0.82 were 94.1% and 92.1%, respectively. Multivariate analysis revealed that μQFRpost (adjusted odds ratio [OR], 1.48; p = 0.002), collateral score (adjusted OR, 6.97; p = 0.01), and current smoking status (adjusted OR, 0.03; p = 0.01) were independently associated with perfusion improvement after treatment.

Conclusions

μQFR was associated with CTP in patients with symptomatic anterior circulation ICAS and may be a potential marker for real-time hemodynamic evaluation during interventional procedures.

Key Points

• Murray law–based QFR (μQFR) is associated with CT perfusion parameters in intracranial atherosclerotic stenosis and can differentiate hypoperfusion from normal perfusion.

• Post-intervention μQFR, collateral score, and current smoking status are independent factors associated with improved perfusion after treatment.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

CBF:

Cerebral blood flow

CBV:

Cerebral blood volume

CI:

Confidence interval

CTP:

CT perfusion

DS:

Diameter stenosis

FFR:

Fractional flow reserve

ICA:

Internal carotid artery

ICAS:

Intracranial atherosclerotic stenosis

ICC:

Intraclass correlation coefficient

IQR:

Interquartile range

MCA:

Middle cerebral artery

μQFR:

Murray law-based quantitative flow ratio

MTT:

Mean transit time

NPV:

Negative predictive value

OR:

Odds ratio

PPV:

Positive predictive value

PTA:

Percutaneous transluminal angioplasty

PTAS:

Percutaneous transluminal angioplasty and stenting

QFR:

Quantitative flow ratio

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVD:

Singular value decomposition

TIA:

Transient ischemic attack

TTP:

Time to peak

References

  1. Gutierrez J, Turan TN, Hoh BL, Chimowitz MI (2022) Intracranial atherosclerotic stenosis: risk factors, diagnosis, and treatment. Lancet Neurol 21:355–368

    Article  CAS  PubMed  Google Scholar 

  2. Qureshi AI, Al-Senani FM, Husain S et al (2012) Intracranial angioplasty and stent placement after stenting and aggressive medical management for preventing recurrent stroke in intracranial stenosis (SAMMPRIS) trial: present state and future considerations. J Neuroimaging 22:1–13

    Article  PubMed  Google Scholar 

  3. Stapleton CJ, Chen YF, Shallwani H et al (2020) Submaximal angioplasty for symptomatic intracranial atherosclerotic disease: a meta-analysis of peri-procedural and long-term risk. Neurosurgery 86:755–762

    Article  PubMed  Google Scholar 

  4. Wabnitz AM, Derdeyn CP, Fiorella DJ et al (2019) Hemodynamic markers in the anterior circulation as predictors of recurrent stroke in patients with intracranial stenosis. Stroke 50:143–147

    Article  Google Scholar 

  5. Song X, Qiu H, Wang S, Cao Y, Zhao J (2022) Hemodynamic and geometric risk factors for in-stent restenosis in patients with intracranial atherosclerotic stenosis. Oxid Med Cell Longev 2022:6951302

    Article  PubMed  PubMed Central  Google Scholar 

  6. Neumann FJ, Sousa-Uva M, Ahlsson A et al (2019) 2018 ESC/EACTS guidelines on myocardial revascularization. Eur Heart J 40:87–165

    Article  PubMed  Google Scholar 

  7. Han YF, Liu WH, Chen XL et al (2016) Severity assessment of intracranial large artery stenosis by pressure gradient measurements: a feasibility study. Catheter Cardiovasc Interv 88:255–261

    Article  PubMed  Google Scholar 

  8. Miao Z, Liebeskind DS, Lo W et al (2016) Fractional Flow assessment for the evaluation of intracranial atherosclerosis: a feasibility study. Interv Neurol 5:65–75

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zanaty M, Rossen JD, Roa JA et al (2020) Intracranial atherosclerosis: a disease of functional, not anatomic stenosis? How trans-stenotic pressure gradients can help guide treatment. Oper Neurosurg (Hagerstown) 18:599–605

    Article  PubMed  Google Scholar 

  10. Tu S, Westra J, Adjedj J et al (2020) Fractional flow reserve in clinical practice: from wire-based invasive measurement to image-based computation. Eur Heart J 41:3271–3279

    Article  PubMed  Google Scholar 

  11. Xu B, Tu S, Qiao S et al (2017) Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis. J Am Coll Cardiol 70:3077–3087

    Article  PubMed  Google Scholar 

  12. Westra J, Tu S, Winther S et al (2018) Evaluation of coronary artery stenosis by quantitative flow ratio during invasive coronary angiography: the WIFI II study (wire-free functional imaging II). Circ Cardiovasc Imaging 11:e007107

    Article  PubMed  PubMed Central  Google Scholar 

  13. Xu B, Tu S, Song L et al (2021) Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial. Lancet 398:2149–2159

    Article  PubMed  Google Scholar 

  14. Tu S, Ding D, Chang Y, Li C, Wijns W, Xu B (2021) Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: a novel method based on bifurcation fractal law. Catheter Cardiovasc Interv 97(Suppl 2):1040–1047

    Article  PubMed  Google Scholar 

  15. Huang K, Yao W, Du J et al (2022) Functional assessment of cerebral artery stenosis by angiography-based quantitative flow ratio: a pilot study. Front Aging Neurosci 14:813648

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kang K, Zhang Y, Shuai J et al (2021) Balloon-mounted stenting for ICAS in a multicenter registry study in China: a comparison with the WEAVE/WOVEN trial. J Neurointerv Surg 13:894–899

    Article  PubMed  Google Scholar 

  17. Kudo K, Terae S, Katoh C et al (2003) Quantitative cerebral blood flow measurement with dynamic perfusion CT using the vascular-pixel elimination method: comparison with H2(15)O positron emission tomography. AJNR Am J Neuroradiol 24:419–426

    PubMed  PubMed Central  Google Scholar 

  18. Damasio H (1983) A computed tomographic guide to the identification of cerebral vascular territories. Arch Neurol 40:138–142

    Article  CAS  PubMed  Google Scholar 

  19. Lan L, Leng X, Abrigo J et al (2016) Diminished signal intensities distal to intracranial arterial stenosis on time-of-flight MR angiography might indicate delayed cerebral perfusion. Cerebrovasc Dis 42:232–239

    Article  PubMed  Google Scholar 

  20. Huang CC, Chen YH, Lin MS et al (2013) Association of the recovery of objective abnormal cerebral perfusion with neurocognitive improvement after carotid revascularization. J Am Coll Cardiol 61:2503–2509

    Article  PubMed  Google Scholar 

  21. Yoshie T, Ueda T, Takada T, Nogoshi S, Fukano T, Hasegawa Y (2016) Prediction of cerebral hyperperfusion syndrome after carotid artery stenting by CT perfusion imaging with acetazolamide challenge. Neuroradiology 58:253–259

    Article  PubMed  Google Scholar 

  22. Tan JC, Dillon WP, Liu S, Adler F, Smith WS, Wintermark M (2007) Systematic comparison of perfusion-CT and CT-angiography in acute stroke patients. Ann Neurol 61:533–543

    Article  PubMed  Google Scholar 

  23. Ge X, Zhao H, Zhou Z et al (2019) Association of Fractional Flow on 3D-TOF-MRA with Cerebral Perfusion in Patients with MCA Stenosis. AJNR Am J Neuroradiol 40:1124–1131

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Faul F, Erdfelder E, Buchner A, Lang AG (2009) Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 41:1149–1160

    Article  PubMed  Google Scholar 

  25. Leng X, Wong KS, Liebeskind DS (2014) Evaluating intracranial atherosclerosis rather than intracranial stenosis. Stroke 45:645–651

    Article  PubMed  PubMed Central  Google Scholar 

  26. Derdeyn CP, Grubb RL Jr, Powers WJ (1999) Cerebral hemodynamic impairment: methods of measurement and association with stroke risk. Neurology 53:251–259

    Article  CAS  PubMed  Google Scholar 

  27. Cianfoni A, Colosimo C, Basile M, Wintermark M, Bonomo L (2007) Brain perfusion CT: principles, technique and clinical applications. Radiol Med 112:1225–1243

    Article  CAS  PubMed  Google Scholar 

  28. Bisdas S, Nemitz O, Berding G et al (2006) Correlative assessment of cerebral blood flow obtained with perfusion CT and positron emission tomography in symptomatic stenotic carotid disease. Eur Radiol 16:2220–2228

    Article  PubMed  Google Scholar 

  29. Shinohara Y, Ibaraki M, Ohmura T et al (2010) Whole-brain perfusion measurement using 320-detector row computed tomography in patients with cerebrovascular steno-occlusive disease: comparison with 15O-positron emission tomography. J Comput Assist Tomogr 34:830–835

    Article  PubMed  Google Scholar 

  30. Tu S, Westra J, Yang J et al (2016) Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: the international multicenter FAVOR pilot study. JACC Cardiovasc Interv 9:2024–2035

    Article  PubMed  Google Scholar 

  31. Boutelier T, Kudo K, Pautot F, Sasaki M (2012) Bayesian hemodynamic parameter estimation by bolus tracking perfusion weighted imaging. IEEE Trans Med Imaging 31:1381–1395

    Article  PubMed  Google Scholar 

  32. Kudo K, Boutelier T, Pautot F et al (2014) Bayesian analysis of perfusion-weighted imaging to predict infarct volume: comparison with singular value decomposition. Magn Reson Med Sci 13:45–50

    Article  PubMed  Google Scholar 

  33. Ichikawa S, Yamamoto H, Morita T (2021) Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke. Radiol Med 126:795–803

    Article  PubMed  Google Scholar 

Download references

Funding

This study has received funding from the Shanghai “Rising Stars of Medical Talent” Youth Development Program (SHWRS (2020)_087), Science and Technology Commission of Shanghai Municipality Explorer Project (22TS1400600), and National Natural Science Foundation of China (82271942).

Author information

Authors and Affiliations

  1. Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shiteng Suo, Zichen Zhao & Shengxian Tu

  2. Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Road, Pudong New District, Shanghai, 200127, China

    Shiteng Suo, Huilin Zhao, Jin Zhang, Jianrong Xu & Yan Zhou

  3. Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Bing Zhao

  4. Shanghai Med-X Engineering Research Center, Shanghai Jiao Tong University, Room 123, No. 1954, Hua Shan Road, Xuhui District, Shanghai, 200030, China

    Shengxian Tu

Authors
  1. Shiteng Suo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zichen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huilin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianrong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shengxian Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yan Zhou or Shengxian Tu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Shengxian Tu.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Shengxian Tu reported research grants and consultancy from Pulse Medical. All other authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

• performed at one institution

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 334 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suo, S., Zhao, Z., Zhao, H. et al. Cerebral hemodynamics in symptomatic anterior circulation intracranial stenosis measured by angiography-based quantitative flow ratio: association with CT perfusion. Eur Radiol 33, 5687–5697 (2023). https://doi.org/10.1007/s00330-023-09557-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-09557-5

Keywords

Search

Navigation