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Automated Quantification of Blood Flow Velocity from Time-Resolved CT Angiography

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Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2018, CVII 2018, STENT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11043))

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Abstract

Contrast-enhanced computed tomography angiography (CE-CTA) provides valuable, non-invasive assessment of lower extremity peripheral arterial disease (PAD). The advent of wide beam CT scanners has enabled multiple CT acquisitions over the same structure at a high frame rate, facilitating time-resolved CTA acquisitions. In this study, we investigate the technical feasibility of automatically quantifying the bolus arrival time and blood velocity in the arteries below the knee from time-resolved CTA. Our approach is based on arterial segmentation and local estimation of the bolus arrival time. The results are compared to values obtained through manual reading of the datasets and show good agreement. Based on a small patient study, we explore initial utility of these quantitative measures for the diagnosis of lower extremity PAD.

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Author information

Authors and Affiliations

  1. Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050, Brussels, Belgium

    Pieter Thomas Boonen & Jef Vandemeulebroucke

  2. Department of Radiology, Vrije Universiteit Brussel (VUB), Laarbeeklaan 101, 1090, Brussels, Belgium

    Pieter Thomas Boonen, Nico Buls, Gert Van Gompel, Yannick De Brucker & Johan De Mey

  3. imec, Kapeldreef 75, 3001, Leuven, Belgium

    Pieter Thomas Boonen & Jef Vandemeulebroucke

  4. Department of Vascular Surgery, Vrije Universiteit Brussel (VUB), Laarbeeklaan 101, 1090, Brussels, Belgium

    Dimitri Aerden

Authors
  1. Pieter Thomas Boonen
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  2. Nico Buls
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  3. Gert Van Gompel
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  4. Yannick De Brucker
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  5. Dimitri Aerden
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  6. Johan De Mey
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  7. Jef Vandemeulebroucke
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Corresponding author

Correspondence to Pieter Thomas Boonen .

Editor information

Editors and Affiliations

  1. University College London, London, UK

    Danail Stoyanov

  2. University of Leeds, Leeds, UK

    Zeike Taylor

  3. University of Barcelona, Barcelona, Spain

    Simone Balocco

  4. University of Bern, Bern, Switzerland

    Raphael Sznitman

  5. Sunnybrook Health Science Centre, Toronto, ON, Canada

    Anne Martel

  6. German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany

    Lena Maier-Hein

  7. Ecole de Technologie Superieure, Montreal, QC, Canada

    Luc Duong

  8. Technical University of München, Garching, Bayern, Germany

    Guillaume Zahnd

  9. Technical University Munich, Munich, Germany

    Stefanie Demirci

  10. Technical University of München, Garching, Bayern, Germany

    Shadi Albarqouni

  11. Imperial College London, London, UK

    Su-Lin Lee

  12. University College London, London, UK

    Stefano Moriconi

  13. Eindhoven University of Technology, Eindhoven, The Netherlands

    Veronika Cheplygina

  14. Technical University of Munich, Garching, Germany

    Diana Mateus

  15. University of Dundee, Dundee, UK

    Emanuele Trucco

  16. École de Technologie Supérieure, Montréal, QC, Canada

    Eric Granger

  17. Universite de Rennes 1, Rennes, France

    Pierre Jannin

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Boonen, P.T. et al. (2018). Automated Quantification of Blood Flow Velocity from Time-Resolved CT Angiography. In: Stoyanov, D., et al. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS CVII STENT 2018 2018 2018. Lecture Notes in Computer Science(), vol 11043. Springer, Cham. https://doi.org/10.1007/978-3-030-01364-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-01364-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01363-9

  • Online ISBN: 978-3-030-01364-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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