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Angio-AI: Cerebral Perfusion Angiography with Machine Learning

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

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Abstract

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).

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Authors and Affiliations

  1. Department of Neurology, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA

    Ebrahim Feghhi, Yinsheng Zhou, John Tran, David S. Liebeskind & Fabien Scalzo

Authors
  1. Ebrahim Feghhi
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  2. Yinsheng Zhou
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  3. John Tran
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  4. David S. Liebeskind
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  5. Fabien Scalzo
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Corresponding author

Correspondence to Fabien Scalzo .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Feghhi, E., Zhou, Y., Tran, J., Liebeskind, D.S., Scalzo, F. (2019). Angio-AI: Cerebral Perfusion Angiography with Machine Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_27

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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