Angio-AI: Cerebral Perfusion Angiography with Machine Learning

  • Ebrahim Feghhi
  • Yinsheng Zhou
  • John Tran
  • David S. Liebeskind
  • Fabien ScalzoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11844)


Angiography is a medical imaging technique used to visualize blood vessels. Perfusion angiography, where perfusion is defined as the passage of blood through the vasculature and tissue, is a computational tool created to quantify blood flow from angiography images. Perfusion angiography is critical in areas such as stroke diagnosis, where identification of areas with low blood flow and where assessment of revascularization are essential. Currently, perfusion angiography is performed through deconvolution methods that are susceptible to noise present in angiographic imaging. This paper introduces a machine learning-based formulation to perfusion angiography that can greatly speed-up the process. Specifically, kernel spectral regression (KSR) is used to learn the function mapping between digital subtraction angiography (DSA) frames and blood flow parameters. Model performance is evaluated by examining the similarity of the parametric maps produced by the model as compared those obtained via deconvolution. Our experiments on 15 patients show that the proposed Angio-AI framework can reliably compute parametric cerebral perfusion characterization in terms of cerebral blood volume (CBV), cerebral blood flow (CBF), arterial cerebral blood volume, and time-to-peak (TTP).


Perfusion angiography Machine learning Digital Subtraction Angiography Stroke 


  1. 1.
    Altman, D.G., Bland, J.M.: Measurement in medicine: the analysis of method comparison studies. J. R. Stat. Soc. Ser. D (Stat.) 32(3), 307–317 (1983). Scholar
  2. 2.
    Cai, D., He, X., Han, J.: Spectral regression for efficient regularized subspace learning. In: ICCV (2007).
  3. 3.
    Cunli, Y., Khoo, L.S., Lim, P.J., Lim, E.H.: CT angiography versus digital subtraction angiography for intracranial vascular pathology in a clinical setting. Med. J. Malays. 68(5), 415 (2013)Google Scholar
  4. 4.
    Hanley, M., Zenzen, W., Brown, M., Gaughen, J., Evans, A.: Comparing the accuracy of digital subtraction angiography, CT angiography and MR angiography at estimating the volume of cerebral aneurysms. Interv. Neuroradiol. 14(2), 173–177 (2008)CrossRefGoogle Scholar
  5. 5.
    Ho, K.C., Scalzo, F., Sarma, K.V., Speier, W., El-Saden, S., Arnold, C.: Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images. J. Med. Imaging (Bellingham) 6(2), 026001 (2019)Google Scholar
  6. 6.
    Ho, K.C., Speier, W., Zhang, H., Scalzo, F., El-Saden, S., Arnold, C.W.: A machine learning approach for classifying ischemic stroke onset time from imaging. IEEE Trans. Med. Imaging 38(7), 1666–1676 (2019)CrossRefGoogle Scholar
  7. 7.
    Liebeskind, D.S., et al.: Abstract WP39: perfusion angiography in TREVO2: quantitative reperfusion after endovascular therapy in acute stroke. Stroke 44, AWP39 (2013)Google Scholar
  8. 8.
    McKinley, R., Hung, F., Wiest, R., Liebeskind, D.S., Scalzo, F.: A machine learning approach to perfusion imaging with dynamic susceptibility contrast MR. Front. Neurol. 9, 717 (2018)CrossRefGoogle Scholar
  9. 9.
    Musuka, T.D., Wilton, S.B., Traboulsi, M., Hill, M.D.: Diagnosis and management of acute ischemic stroke: speed is critical. CMAJ 187(12), 887–893 (2015)CrossRefGoogle Scholar
  10. 10.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). Scholar
  11. 11.
    Prabhakaran, S., Ruff, I., Bernstein, R.A.: Acute stroke intervention: a systematic review. JAMA 313(14), 1451–1462 (2015)CrossRefGoogle Scholar
  12. 12.
    Scalzo, F., Hao, Q., Alger, J.R., Hu, X., Liebeskind, D.S.: Regional prediction of tissue fate in acute ischemic stroke. Ann. Biomed. Eng. 40(10), 2177–2187 (2012)CrossRefGoogle Scholar
  13. 13.
    Scalzo, F., Liebeskind, D.S.: Perfusion angiography in acute ischemic stroke. Comput. Math. Methods Med. 2016, 14 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Yu, Y., Guo, D., Lou, M., Liebeskind, D., Scalzo, F.: Prediction of hemorrhagic transformation severity in acute stroke from source perfusion MRI. IEEE Trans. Biomed. Eng. 65(9), 2058–2065 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ebrahim Feghhi
    • 1
  • Yinsheng Zhou
    • 1
  • John Tran
    • 1
  • David S. Liebeskind
    • 1
  • Fabien Scalzo
    • 1
    Email author
  1. 1.Department of NeurologyUniversity of California, Los Angeles (UCLA)Los AngelesUSA

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